Mobile Devices for Banknote Authentication

Mobile Devices for Banknote Authentication – Is it Possible? Volker Lohweg 1a, Roland Hildebranda, Helene Dörksena, Eugen Gillicha, Jan Leif Hoffmanna, and Johannes Schaedeb a inIT–Institute Industrial IT, Ostwestfalen-Lippe University of Applied Sciences, Liebigstr. 87, D-32657 Lemgo, Germany; b KBA-NotaSys S.A., Avenue du Grey 55, CH-1018 Lausanne, Switzerland ABSTRACT Maintaining confidence in security documents, especially banknotes, is and remains a major concern for the central banks in order to maintain the stability of the economy around the world. In this paper we describe an image processing and pattern recognition approach which is based on the Sound-of-Intaglio concept [1] for the usage in smart devices such as smartphones. Today, in many world regions smartphones are in use. These devices become more and more computing units, equipped with resource-limited but effective CPUs, cameras with illumination, and flexible operating systems. Hence, it appears to be obvious, to apply those smartphones for banknote authentication, especially for visually impaired persons. However, it has to be researched, whether those devices are capable of processing the data under the constraints of image quality and processing power. Our results show that it is in general possible to use such devices for banknote authentication applications. Keywords: authentication, anti-counterfeit features, mobile device, smartphone, wavelet transform, pattern recognition, Sound-of-Intaglio

1. INTRODUCTION Maintaining confidence in security documents, especially banknotes, is and remains a major concern for the central banks in order to maintain the stability of the economy around the world. So far the vast majority of counterfeits retrieved by police forces and banks are created with methods and equipment which are commercially available. In addition to the “proved” mass counterfeits on commercial offset presses the continuous progress in digital desktop technologies (scanners, cameras, and digital office printers) has established a complete new class of “digital” counterfeits (digifeits). Intaglio has proved to be the most reliable and secure platform for defence against counterfeits. Though the Intaglio features are not consciously recognised by the public, the unmistaken optical appearance in combination with the unique tactile properties (both to be seen in combination with the printing substrate) is the key to the habitual recognition of genuine notes for the users. So far, the counterfeit technologies are unsuccessful in providing acceptable simulations of Intaglio or even to use the technology for criminal purpose. Due to the strict non-proliferation policy in the industry, the high definition banknote Intaglio process in its totality (design, origination, plate making and printing) is well contained against its use or abuse in counterfeit applications. With the uniqueness of the Intaglio process for the security of banknotes, its unmistakable appearance and the function in public circulation it is obvious to attempt to directly identify genuine banknotes by identifying the presence of Intaglio. As the direct measurement of 3D-structures under the rough and challenging conditions of circulation have proved to be difficult and lacking robustness, a complete different approach has been sought, which exploits the unique opacity and appearance of common high quality Intaglio structures. To use this avenue is also attractive, as central banks – for understandable and obvious reasons – are reluctant to publish the characteristics of their “covert” features. As the disclosure of such feature characteristics would be a “vade mecum” for the criminal fraternity, the authentication in automated circulation is limited to the assessments of the commercial companies providing such services. Any other attempt to authenticate the banknotes by using the “overt” features has not been successful. Therefore using the Intaglio in itself would also exploit the most important overt feature platform for machine authentication [2]. As reported in [2, 3] Intaglio is one of the most prominent security features which can be handled by both, the visually non-impaired and impaired persons. This observation underlines the 1

[email protected]; phone +49-5261-702-408; fax +49-5261-702-312; http://www.init-owl.de.

Volker Lohweg, Roland Hildebrand, Helene Dörksen, Eugen Gillich, Jan Leif Hoffmann, and Johannes Schaede

2

importance of Intaglio as it is the subconscious habitual recognition which makes it popular as an intrinsic feature. Though no statistical data are available, any attempt to leave out Intaglio in the past has regularly led to major counterfeit attacks. The importance of Intaglio is also confirmed by the fact that typical features are not known in details in the public [3]. Although approx. 80% of the Europeans are aware of certain overt security features in the EURO banknote, they are in most cases not able to describe details for a certain overt feature (pars-pro-toto-effect) [4]. Assuming that the statistical data which have been recently collected in the Netherlands are representative in some way for Europe, then three main overt security features, besides Intaglio, are more or less actively known by the public: watermark (65%), kinegram/silver foil (43%), and ultra-violet (UV) effects, like fluorescent fibres, etc. (18%). The percentage value in parentheses describes the active awareness of a certain feature in the public. As reported before, in most cases the public was not able to e.g. describe the details of the watermark content in the EURO banknotes. The same human behaviour occurs for the EURO themes of the banknotes (windows/doors and bridges) [2, 3]. Therefore, it is self-evident to use Intaglio as a prominent intrinsic feature for banknote authentication. In [5] a concept for intrinsic feature generation was recently published, which will be the base of the paper at hand. The present approach is predicated on the observation that Intaglio is used by tactile and/or optical perception of the line structures of a banknote which are recognisable by a co-knowledgeable individual and/or by habitual knowledge of the (visually non-impaired and impaired) public [6]. In this paper we describe an image processing and pattern recognition approach which is based on the Sound-ofIntaglio concept [5] for the usage in smart devices such as smartphones. Today, in many world regions smartphones are in use. These devices become more and more computing units, equipped with resourse-optimised CPUs, cameras with illumination and other well-known features and mobile operating systems. Hence, it is obvious, to apply those smartphones for banknote authentication. However, it has to be researched, whether those devices are capable of processing data under the constraints of image quality and processing power. The paper at hand is organised as follows: After the introduction, the related work will be highlighted in the second section where we focus on the technology state-of-the-art of mobile phones, operating systems, banknote applications on smartphones, and Wavelet-based Intaglio Detection. In the third section our approach on banknote authentication for smartphones will be pointed out. The fourth section is dedicated to implementation aspects and results, and the fifth section concludes the paper and gives an outlook to the future work.

2. PREREQUISITES AND RELATED WORK 2.1 Mobile devices technology and market share Mobile devices such as smartphones experience a rapid development in all areas of industrial and private sector applications. Driven by commercial and tele-communication products the Integrated Circuit (IC) manufacturers present powerful electronics in short time periods. Based on Moore’s assumption 2 [7], electronic devices double their integration grade approx. every 18 months which leads to the fact that mobile devices become more and more efficient and versatile. Figure 1 shows two smartphones produced and market-launched in 2008 and 2011, respectively. The technical data of these devices underlie the rapid development in this area.

Fig. 1 – G1 (HTC) from autumn 2008 (left) and Nexus (Google, Samsung) from autumn 2011 (right).

2

The above mentioned prognosis (Moore‘s assumption (1965): „The integration grade (transistors per area) will be doubled every 18 months“) is sometimes also called „Moore‘s Law“, which is misleading, because the assumption is not confirmed by a scientific theory. However, the assumption is obviously true in the long run.

Optical Document Security, San Francisco, 18 – 20 January 2012

© Reconnaissance International 2012

Mobile Devices for Banknote Authentication – Is it Possible?

3

The G1 smartphone is characterised by the following data: 528 MHz processor core, 192 MB RAM, 3.2“ display (480 × 320 pixel), BT, WLAN, GPS, and digital compass. The Galaxy Nexus is characterised by the data below: 2 x 1.2 GHz processor cores, 1 GB RAM, 4.65“ display (1280 × 720 pixel), BT, WLAN, NFC, GPS, digital compass, acceleration, approximation, ambient light sensor, and gyroscope. Furthermore, state-of-the-art smartphones are equipped with camera hardware with a large variety of image quality. All cameras generate noisy images in the case of low ambient illumination or deactivated LED-light. This fact is a challenge for the downstream image processing algorithms. One major drawback of most of the smartphones is based on the fact that the on-board image pre-processing units create a massively sharpened output image. Those effects have to be reduced, or better deactivated, when a smartphone is used for image processing applications. A typical example is the iPhone 4 by Apple which generates extremely sharpened and noisy images. In the case of an excellent illumination the iPhone 4 creates dynamic images with large colour saturation. Though, using the integrated LED-light, the images are disturbed by locally changing colour hue effects. Sony Ericsson’s Xperia arc and Xperia Ray comes up with a high quality camera unit. Under daylight conditions the smartphone delivers the very good images in its class. However, this smartphone lacks in quality under low ambient light conditions without LED-Light. This effect can be monitored for nearly all phones. Other smartphones like Nexus One, HTC Desire, Samsung Galaxy S, just to name a few, perform fair regarding the camera systems. The technology itself grows rapidly. Though, the camera units have to be improved in the next years by the manufacturers. However, also today it is possible to apply these devices for banknote authentication, if the algorithms can be adopted according to the technical necessities for the smartphone at hand. Table 1 shows different types of smartphones with their camera resolutions and on-board processing units. Table 1 – Different smartphones with their key features for image processing applications. Processor

Sensor / Megapixel

Autofocus

Apple iPhone 4

1 GHz Single-Core

5



Apple iPhone 4s

1 GHz Dual-Core

8



HTC Desire HD

1 GHz Single-Core

8



HTC Sensation

1,2 GHz Dual-Core

8



HTC Sensation XE

1,5 GHz Dual-Core

8



HTC Sensation XL

1,5 GHz Single-Core

8



LG P990 Optimus Speed

1 GHz Dual-Core

8



Motorola Atrix

1 GHz Dual-Core

5



1,2 GHz Dual-Core

8



680 MHz Single-Core

12



1,4 GHz Dual-Core

8



1,4 GHz Single-Core

4.9



Samsung Galaxy S i9000

1 GHz Single-Core

4.9



Samsung Galaxy S2 9100

1,2 GHz Dual-Core

8



Samsung Galaxy W i8150

1,4 GHz Single-Core

4,9



Sony Ericsson Xperia arc

1 GHz Single-Core

8



1,4 GHz Single-Core

8



1 GHz Single-Core

8



Model

Motorola RAZR Nokia N8 Samsung Galaxy Note N7000 Samsung Galaxy Plus i9001

Sony Ericsson Xperia arc S Sony Ericsson neo

According to Gartner the worldwide vendor shipments of mobile devices in the second quarter 2011 are quantified with 429 million, including 108 million smartphones. In the third quarter 2011 the shipments raised to 441 million, including 115 million smartphones [8]. Based on the research from Strategic Analytics [9] the smartphone market has grown to 24 million devices in China during the third quarter 2011 which leads to a quarter-to-quarter growth of




4

+57.5%, whereas the US market decreased by -7.4% quarter-to-quarter growth and leads to 23 million units. Furthermore, the following apt quotation shows the market dynamic in this technology area: “The United States remains the world’s largest smartphone market by revenue, but China has overtaken the United States in terms of volume. China is now at the forefront of the worldwide mobile computing boom. China has become a large and growing smartphone market that no hardware vendor, component maker or content developer can afford to ignore.” – Neil Mawston, Executive Director at Strategy Analytics in November 2011 [9]. 2.2 Mobile operating systems Mobile Operating Systems (MOS or Mobile OS) control mobile devices like other computer systems are controlled and managed by Operating Systems, such as Unix, Linux, OS-9000, Windows, and others. The difference between standard operating systems and Mobile OS is substantiated by the fact that the communication channels are mainly designed for wireless interconnections like UMTS, WiMAX, and LTE, just to name a few [10]. Furthermore, the hardware units in smartphones are mainly designed as embedded systems, and therefore, the Mobile OS have to be resource efficient regarding their memory space [11]. In the following paragraph the main Mobile OS will be highlighted. However, it has to be pointed out here that many other Mobile OS exist besides the below mentioned. •

• • • • •

Android – This Mobile OS is Linux-derived, partly free and open source. It is supported by Google and other hardware and software vendors like ARM, HTC, Motorola, Samsung, and others. With the introduction of HTC smartphones the amount of devices is increasing rapidly. Though, Android is open source, parts of the application software can be closed, e.g. the camera units in many smartphones [12]. Symbian – Symbian OS by the Symbian Foundation is an open public licence OS backed by Nokia. It is used by many vendors, e.g. Nokia, Mitsubishi, BenQ, LG, just to name a few [13]. The market share is currently declining. iOS (iphone OS) – The Apple Mobile OS is derived from Apple’s Mac OS X and is closed source as well as proprietary [14]. BlackBerry OS – This OS is closed source and proprietary and was mainly developed for BlackBerry phones. As its manufacturer Research in Motion (RIM) has taken over QNX, a vendor of a UNIX-like OS, it is assumed that BlackBerry OS could be replaced by QNX. [15]. Windows Phone – Windows Phone 7.5 is a Mobile OS by Microsoft and successor of the Windows-CE-based Windows Mobile 6.5. It includes many Microsoft applications. It is closed source and proprietary [16]. Bada – Bada OS is a close source and proprietary Mobile OS developed by Samsung. The OS has a kernel configurable architecture which allows users to change the OS kernel [17].

In Figure 2 the worldwide market share of Mobile OS based on mobile phones is displayed for the years 2010 and 2011. It is noticeable that the market share of Android has increased from 25.3% to 52.5% over the last two years. This fact is equivalent to approx. 60 million mobile phones which are equipped with Android OS in the third quarter 2011. A massive decrease from 36.3% to 16.9% is observed for Symbian OS, whereas iOS and RIM is only decreasing a little. Symbian was expected to experience a loss with Nokia’s move to Mircosoft Phone. Mircosoft’s market share declined from 2.2% in 2010 to 1.7% in 2011. The future market share of Microsoft’s Phone OS will presumably increase with Nokia’s move. Bada’s share has increased to 2.2% in 2011 [18, 19]. 60,0 50,0 40,0 Market share in % 30,0

2010 2011

20,0 10,0 0,0

Android

Symbian

iOS (iphone Research in OS) Motion (RIM)

Microsoft

Bada

others

Fig. 2 – Worldwide market share of implemented Mobile OS in the years 2010 and 2011 [18, 19].




5

2.3 Banknote applications for smartphones The vision of using mobile devices for banknote authentication is not new as such. Different papers have cited such kind of applications, e.g. [3, 5, and 20]. The basic idea is to use the integrated camera, the illumination unit, and the processing unit to analyse different overt and covert banknote features and to classify the banknotes. Another approach was recently published which is based on a pocket scanner equipped with UV-visible, an optical near infrared point light sources, and a low power sensor chip. This system can be connected to any mobile phone [20]. The technology imitates some of the basic concepts of ATM manufacturers. In the next paragraphs we highlight some applications which are connected to banknotes and smartphone applications to show how fast applications for smartphones are growing. We highlight here Apple’s iPhone Apps as typical examples. However, only one of them is capable to perform a banknote authentication using UV-light. Besides these Apps some more exists which can be used as banknote presentation applications, e.g. [21, 22]. • EuroCheck is an App which checks the serial number on EURO banknotes regarding “serial number correct length; Check digit verification obtained by mathematical operations on the serial number; presence or absence of the serial number in the Police Force Data Bank counterfeit notes archives, provided by the Italian Ministry of Interior” [23]. It is self-evident that copied banknotes with a valid serial number are not detectable. • Banknote or Not?-App tries to compare the serial number of a said banknote [24]. It is self-evident that copied banknotes with a valid serial number are not detectable. • Bill Sound Scanner-App states to detect different of the main currencies by the paper sound. The developer comments: “However, Bill Sound Scanner will only recognize the sound produced by the paper of the banknote. You must check the other characteristics to ensure that it is not a counterfeit. The application may not be relevant for your banknotes if the quality of the paper is not good enough. Do not rely on this app to authenticate bills” [25]. Tests have shown that this App does not seem to work. • MoneyScan Pro is an App which simulates a UV lamp by filtering the illumination light. Therefore, it is possible to detect UV effects on a banknote which is assumed to be authentic [26]. • Counterfeit is another App which presents different EURO banknote counterfeits. The user can use this App by visible inspection of a banknote and the presented counterfeit note [27]. • EyeNote is one of the prominent Apps developed by the Bureau of Engraving and Printing (BEP). The BEP claims the following: “EyeNote is a mobile device application to denominate Federal Reserve Notes (U.S. paper currency) as an aid for the blind or visually impaired to increase accessibility. Using EyeNote, a user can have the denomination of a note scanned and communicated back to the user. EyeNote works on the iPhone 3G and 3Gs, the iPhone 4 and 4S, the 4th Generation iPod touch, and the iPad 2. This application does not authenticate a note as either real or counterfeit. Please refer to the license agreement on the iTunes App Store” [28]. • LookTel Money App “instantly recognizes currency and speaks the denomination, enabling people with visual impairments or blindness to quickly and easily identify and count bills” [29]. 2.4 Wavelet-based Intaglio detection Preferably, the decomposition of the image is carried out by performing digital signal processing techniques based on so-called Wavelets. In this subsection the general concept of Wavelet-based Intaglio Detection (WBID) is described. For further details of the concept and variants we have to refer to the corresponding literature because of space limitations. 2.4.1 Shift-invariant wavelets A Wavelet is a mathematical function used to divide a given function or signal into different scale components. A Wavelet transformation (or Wavelet transform) is the representation of the function or signal by Wavelets. Wavelet transforms have advantages over traditional Fourier transforms for representing functions and signals that have discontinuities and sharp peaks. According to the present concept, one in particular exploits the properties of so-called discrete Wavelet transforms (DWTs) as it will be discussed in the following. Wavelet theory will not be discussed indepth in the present description as this theory is well-known and is extensively discussed and described in several textbooks on the subject. The interested reader may for instance refer to the cited books and papers about Wavelet theory [30, 31, 32, 34, and 35]. To recognise local features, it is important that the signal transformation is shift invariant. This means a signal shift by ∆ samples may lead to a shift of scaling or detail coefficients, but not to a modification of their values. This property guarantees that a scale diagram does not depend on the selection of the zero point on a scale. Using the Fast Wavelet Transform (FWT), we lose the property due to its inherent sub-sampling. Consequently, wavelet coefficients show a high dependency on signal shifts. By sub-sampling when progressing to the next transformation scale, we also run the risk of forfeiting important information on edges. Hence, it is crucial to apply a signal transformation that is shift




6

invariant. To get a shift invariant transformation, it is self-evident to determine the transformation without the subsampling of a signal s[n] [36, 37]. This condition is met by the shift-invariant Wavelet Transform (SWT). For shifted, but otherwise identical signals, SWTs provide shifted and otherwise identical wavelet coefficients. As no sub-sampling is used a redundant signal representation is gained [36, 37]. For transforming two-dimensional banknote images into spectral descriptions, two one-dimensional transformations are applied [37]. This is valid because images can be interpreted as separable signals [9]. To transform a two-dimensional signal , the one-dimensional transformation algorithm alternately on the image rows n and the image columns m are employed. This results in a square matrix with the dimensions : .

(1)

Now, the wavelet-transformed signal is divided into four sub-images: Scaling coefficients A (lowpass-filtered) and vertical detail coefficients cV (bandpass-filtered) belonging to Ay, and horizontal as well as diagonal detail coefficients (cA and cD, bandpass-filtered) are comprised in Dy [10]. The detail matrices cV, cH, and cD describe the same structure of the wavelet-transformed signal of the image. In a second step the detail coefficients are combined to a general detail matrix cG , (2) being a scale factor which guarantees the same dynamic range for the scaling coefficients and the details with coefficients, if necessary. With cG all recognised structure transitions are united in one matrix. Please note that one cannot retrieve the signal from the united detail coefficients cG. When authenticating banknotes, though, this aspect is irrelevant. To process a Wavelet Transform it is necessary to fit a wavelet to the application. In general, good results are achieved with Daubechies wavelets with two vanishing moments (db-2-wavelet). These wavelets are well suited for spectral analysis of fine Intaglio structures because of their compact support [1]. 2.4.2 Classification features The use of moment-based statistical features of wavelet coefficients is advantageous [1, 5, 38]. In Figure 3 we show based on a typical Intaglio line different normalised greyscale frequency histograms of SWT coefficients structure of a KBA-NotaSys Specimen “Jules Verne” (Figure 4). It is intuitive that the greyscale frequency distribution of genuine banknotes differs considerably from forged ones.

Fig. 3 – Histograms of wavelet coefficients after a db2-SWT: (a) Genuine, (b) High-Quality Forgery, and (c) Low-Quality Forgery. The greyscale frequency distribution of genuine banknotes differ considerably from forged ones.

Fig. 4 – Intaglio line structures: (a) Genuine, (b) High-Quality Forgery, and (c) Low-Quality Forgery.




7

By calculating descriptive measures on standardised histograms H n ( p ) global conclusions on the image structure can be discussed. The following statistical features are taken into account for further analysis of the wavelet coefficients: Variance σ² depicts the amplitude distribution of the wavelet coefficients around the histogram center. Skewness E describes the symmetry of the distribution around the center. Excess C tells the deviation relative to the Gaussian distribution, e.g. [38]. In Figure 5 we show the feature space containing object classes which are to be classified.

genuine high-quality forgery low-quality forgery inappr. ROI

1

feature 3

0.8 0.6 0.4 0.2 0 0

0.2

0.4

0.6

0.8

1

0

0.2

feature 1

0.4

0.6

0.8

1

feature 2

Fig. 5 – Feature space, spanned over σ² (feature 1), E (feature 2), and C (feature 3). The training set consists of 1489 objects [38].

2.4.3 Classification For the general concept in classification we distinguish four different clusters: “Genuine”, “High-Quality Forgery”, “Low-Quality Forgery”, and “Inappropriate Region-of-Interest (ROI)”. The latter describes regions on a banknote that contain no or insufficient structures and thus being inappropriate for authentication. ”Inappropriate ROI” clusters could belong to every banknote type. They can be selected empirically or automatically. Figure 5 implies that in this case classification is not a linearly separable problem. These non-linearly separable data sets can be separated by the use of a Kernel-Support-Vector-Machine (SVM) [39]. For multi-class classification a oneagainst-all classification approach is applied [39]. Please note that an object is bijectively assigned to a class only when its class affiliation appears only once in all classification processes. When a feature vector is assigned to several or no classes, it is discarded. According to the authentication procedure this means that it was not possible to assign the detail bijectively or it is unsuitable. The classification kernel is determined in a way that the authentication process is applicable to other denominations. In Figure 5 it is observable that the individual object clusters show a certain direction. Our approach is to enhance the class borders in such a manner that they stretch or open into the preferred direction. Thus, the area of validity of the classes to cover a multiplicity of denominations can be broadened. For such a problem inhomogeneous polynomial kernels are a good choice. They are able to meet the required characteristics of the cluster parting planes by few support vectors [39]. In the next section a new classification strategy with a linear discriminant method will be discussed. This method has the advantage that the implementation is more efficient for smartphones.

3. APPROACH In this chapter we present a new approach for banknote authentication based on mobile devices. The new approach in banknote authentication is mainly technology driven. On one hand the Wavelet-based Intaglio Detection has to be adapted for the use in smartphones as such; on the other hand the specific technological characteristics of smartphones cameras etc. have to be taken into account.




8

3.1 Introduction The approach is based on the recently published Sound-of-Intaglio concept which focuses on the analysis of intrinsic features produced by Intaglio printing, e.g. [1, 5, 38]. This is due to the fact that Intaglio printing enables the printing of very fine, high resolution and sharply-defined patterns. Also Intaglio is the most resistant printed feature, which gives the methodology a certain advantage in robustness under the conditions of circulation. Therefore, Intaglio is identified “as it is” as an intrinsic feature and can serve as a secure method of identification for the public, e.g. [40, 41, 42, 43]. To further support the public human perception a method is described which identifies the unique features of Intaglio with affordable image analysis tools by using e.g. mobile telephones. Of course the general approach can also be useful for central banks in sorting and forensics. Furthermore, an advantage of the concept is that there is no need for the central banks to disclose any secret information like special properties, geometries etc. and specifically no need to redesign existing banknotes, provided that the Intaglio reaches a certain quality level. One of the main concerns in the detection of banknote forgeries for the public is based on the fact that i) many overt security features are unknown, ii) if an overt feature is known in general, then the optical details are in most cases unknown (exact content of a specific kinegram, a water mark, etc.), and iii) most of the features can not be handled, even if known, in daily life in terms of a level 1 feature (high resolution kinegram, etc.). As the authentication of banknotes has to be handled by the public, there is the need, besides level 1 of defence, to intensify the level 2 of defence by electronic devices. However, it must be assured that the public is able to use such devices without any specific know-how in terms of banknote security. Such devices are state-of-the-art mobile telephones, resp. smartphones. The today’s phones are, generally speaking, able to handle even complex algorithms for e.g. authentication of banknotes. This is basically possible because of the usage of high resolution cameras up to 12 Megapixels, resource-efficient processors, and operating systems like Microsoft’s Windows Mobile 7, Apple’s iOS, and Google’s Android. In terms of signal processing, the fine structures of Intaglio technique can be considered as areas of interest with certain ranges of spatial frequencies. Therefore, a new algorithmic approach based on the Sound-of-Intaglio concept was explored and subsequently researched for mobile devices. However, different challenges and hurdles were faced which were mainly founded by thermal and optical effects. 3.2 Artefacts Applying authentication algorithms for smartphones is strongly correlated with the resolution capability of the integrated camera, optics and internal image processing. As well known from previous research the resolution has to be approx. 600 dpi for a stable intrinsic feature detection of Intaglio [5]. Smartphones in our research have up to 8 Megapixel available. This results in a local resolution of 1,000 dpi. In general, this resolution is enough for banknote authentication. However, it has to be accentuated, that the quality of a smartphone’s optical and processing path is varying not only from product to product, but also from device to device. It can be assumed, to our best knowledge, that Rav ± 25% . This fact the effective resolution which ranges in reference to an average resolution, is approx. R= eff impedes the image processing notably. Furthermore, the following topics have to be taken into account when smartphones are used for optical measurement tasks. Smartphones are not designed for permanent use regarding image capture and processing. The devices grow warm to hot and change therefore different operating points (white balance, hue, thermal noise, etc.). The processors are optimised for broadband communication. Therefore, they are not designed for fast image processing. The operating systems, e.g. Android 2.3.4, do not permit the access of RAW images, although it is stated in different documentations [44]. All full frame images are pre-processed. This fact is one of the big hurdles for our application. Full frame images are usually compressed and sharpened. For image processing RAW images are necessary. Therefore, preview images are used throughout the further research. For all experiments a Sony Ericsson Xperia arc with Android 2.3.4 is used. The WBID algorithm concept has to be extended for smartphones, because of the above mentioned topics. 3.3 Algorithm concept and implementational aspects Android and iOS based smartphones generate only JPEG-images. Such images show due to the fact of comparatively high compression strong artefacts especially in the region of edges. So called JPEG-artefacts distort the high frequency components. This effect has negative impact on the authentication algorithm. Hence, JPEG compressed images are not usable for WBID. However, it is possible to use the so called preview images as a source. The size of the preview image depends on the size of the display and its resolution. A typical resolution is based on the video stream data format which is 1,280 × 720 pixel. Though, these images have a lower resolution (921,600 pixel), they are generated as YCrCbImages. As well known, the Y-channel represents the greyscale intensity which is not compressed [45]. Therefore, these low resolution images are more suitable for authentication. The local resolution is approx. 610 to 630 dpi in a distance




9

of approx. 100 mm from the camera lens to the banknote. A drawback is that it is not possible to capture the whole bank note. Figure 6 shows such an image.

Fig. 6 – Preview image captured from Sony Ericsson arc with 620 dpi in approx. 100 mm distance from camera optics. The visible spacer shows the local distance of one inch.

To assure that the complete banknote, or at least the necessary Intaglio regions, is captured for evaluation, some general concepts are conceivable. An obvious attempt is based on the fact to use the overlay function for the display. If a known banknote is considered, then an overlaid mask can be used to position the smartphone camera over a specific region of the note. As a result it is possible to fall back on the same position for the evaluation of a banknote of a known type (currency and denomination). In our current concept we use three 400 × 400 pixel regions (ROI) with a resolution of approx. 620 dpi which are analysed and classified independently. In the existing case the regions are characterised as intra-homogeneous in terms of Intaglio line width and direction. However, the Intaglio structure types are different from ROI to ROI; this means the homogeneity is varying. The Wavelets which are in use are of db-2-type. We assume that the classification gives a correct result if all three classifiers give the same local results (maximum opinion). For classification we consider three regions with different Intaglio patterns. In Figure 7 these three areas are shown (top, middle, and bottom ROI). As described above, typical statistical moments (variance σ2, skewness E, and curtosis C) of the Wavelet Transform coefficient’s histogram are stated to be especially suitable for the detection of Intaglio printing [38]. The statistical moments strongly depend on the number of Intaglio lines within a ROI. Hence, in general, different regions do not coincide in the feature space and build disjoined clusters (cf. Figure 8). However, elements printed with offset can appear in the background of Intaglio; this biases the histogram. Therefore, the classification should be performed for each region independently to prevent the use of higher order discriminant polynomials. The implementation of such curves can hardly be done as an automated process.

Fig. 7 – Specimen detail image (Y-channel) containing Intaglio, divided in three ROIs (top, middle, and bottom). The regions contain different Intaglio patterns and are used for classification.




10

In Figure 8 the feature space for statistical moments is presented for the top, middle and bottom ROIs of genuine objects. GENUINE region top GENUINE region middle GENUINE region bottom

1

C

0.5

0 0 5 σ2 -0.4

-0.3

E

-0.2

-0.1

10

Fig. 8 – Non-normalised Intaglio feature space with disjoined clusters of genuine objects for the top, middle, and bottom ROI. In each class 66 images were captured. The clusters are separate from each other.

The described advantages are not given if objects of the different regions are considered as indistinguishable for classification. The accuracy of the classification can be doubted which is crucial for the application in banknote authentication. As training data set we took 11 images, acquired by different cameras. (Images from three types of mobile phones by automated and freehand image acquisition) and built the classes GENUINE and FORGERY for each of the three regions with 66 images in each class. Despite such a small training data set, the classification has to be robust against unknown data. In the approach it is decided not to apply the SVM technique because of its complexity [39], but rather to apply the Linear Discriminant Analysis (LDA) [46] technique which is used for robust classification of regions. LDA classifier follows a statistical approach and achieves maximum discrimination by minimising the intra-class distance and maximizing the inter-class distance simultaneously. All elements of classes contribute to the solution uniformly and the possibility of the misclassifying unknown data is fairly low. The LDA solution is especially reliable if the distribution of the classes is Gaussian [47]. We will show that in our case the LDA is suitable for the classification. A so called Lilliefors test [48] (which is an adaptation of Kolmogorov-Smirnov test) for normality testing is performed on the above mentioned data set. The idea for using Lilliefors test was that it might be more reliable than the classical Kolmogorov-Smirnov test [49] or chi-square test [50], since our observations result from non-continuous distributions and the size of the data set is fairly small. Kolmogorov-Smirnov test is specified for observations coming from continuous distributions, and chisquare test is reported to be weak if used with small samples [48, 49, and 51], i.e. the validity of both tests would be questionable. Table 2 – Results for Lilliefors test – GENUINE.

Top region Middle region Bottom region

σ2 normal not normal not normal

E not normal not normal normal

C not normal normal normal

Table 3 – Results for Lilliefors test – FORGERY. Top region Middle region Bottom region

σ2 not normal not normal not normal

E normal normal not normal

C normal normal not normal

It can be concluded that at least one of the classes is Gaussian (cf. Table 2 and Table 3). This supports the idea for using LDA. As we will show below the LDA is suitable for the classification and provides a solution which separates classes well. The LDA classifier is mathematically described as follows. In a feature space f of the dimension d we look

for a direction w = ( w1 , w2 , , wd ) representing linear combinations of the features that separates the class means T




11

optimally (when projected onto the found direction) while achieving the smallest possible variance around these means. The empirical class means for a one-dimensional feature space f of classes genuine G with n objects and forgery F with m objects are 1 (3) m(G ) = ∑ f ∈G f n and 1 (4) m ( F ) = ∑ f ∈F f . m Similarly, the means of the data projected onto some direction w in a higher-dimensional feature space can be computed by 1 T (5) µ (G ) = ∑ f ∈G w f , n and 1 (6) m ( F ) = ∑ f ∈F w T f . m The variances σ 2 (G ) and σ 2 ( F ) of the projected data can be expressed as

= σ 2 (G )

∑ (w

T

f − µ (G ) ) ,

(7)

= σ 2 (F )

∑ (w

T

f − µ (F )) .

(8)

and

f ∈G

f ∈F

2

2

The LDA solution is the direction w* which maximises the optimisation problem

D(w * ) = max

(G ) − ( F ) ) ( mm

w

2

σ 2 (G ) + σ 2 ( F )

.

(9)

Within the described direction w = ( w1 , w2 , , wd ) , representing a linear combination of the features, equation (9) is rewritten as wT S w (10) D(w * ) = max T b w w Sww with the inter- and intra-class co-variances T

Sb = ( m(G ) − m( F ) )( m(G ) − m( F ) )

T

(11)

and

S w=

1 1 T T ∑ ( f − m(G) )( f − m(G) ) + m ∑ f ∈F ( f − m( F ) )( f − m( F ) ) . n f ∈G

(12)

The non-negative real value 0 ≤ D(w * ) ≤ ∞ is called discriminant value; it is also known as Rayleigh coefficient. The LDA solution for the top region has a discriminant 2

Dtop= 6.5, with ( µ (G ) − µ ( F ) ) = 0.8921 , σ 2 (G ) = 0.0271 , σ 2 ( F ) = 0.1101 ; for the middle region

•

•

2

Dmiddle = 8.67 with ( µ (G ) − µ ( F ) ) = 1.6606 , σ 2 (G ) = 0.0787 , σ 2 ( F ) = 0.1129 ;

and for the bottom region •

2

Dbottom = 4.72 with ( µ (G ) − µ ( F ) ) = 0.4277 , σ 2 (G ) = 0.0336 , σ 2 ( F ) = 0.057 .




12

Figure 9 shows feature spaces of genuine and forged objects for the top, middle and bottom ROIs for the features σ2, E and C. It is visible that the separability of these three features is high; however, it is not linear separable, and the LDA solution does not lead to an accurate linear classification. GENUINE region top FORGERY region top

2

C

1.5

1

0.5

-1 0

0 0

0.5

1 E

1.5

2

1

GENUINE region middle FORGERY region middle

1.5

σ

2

GENUINE region bottom FORGERY region bottom

1.5

1

C

C

1

0.5

0.5

0

0 0 -1

0.5 -0.5

0 E

0.5

1

σ2

1

0 -1

0.5 -0.5

0 E

σ2

0.5

1

1

Fig. 9 – The clusters of genuine and forged objects for the top, middle and bottom ROIs in the feature space are presented. The chosen perspective of the feature space shows a good 2D projection. The separability of the classes is visible. In both cases the feature space is normalised to [0,1] for the class GENUINE.

One approach to achieve a more accurate linear classification is to consider additional features. The additional features have to fulfil two important properties. First, they have to be suitable for recognition of Intaglio printing, and second, they have to be complementary to the existing three statistical features. For creating a new feature, we divide the histogram into three meaningful areas: left, middle, and right. The threshold for the middle part is the Standard deviation of the distribution σ. As additional features, we introduce volumes of the meaningful parts of the histogram. The feature can be interpreted as local adaptive cumultative histogram (LACH) feature H i (σ ) , controlled by the variance. The features are illustrated in Figure 10.

Fig. 10 – A typical distribution based on the Wavelet Transform of a genuine object. The features H L , H M , and H R represent the content of meaningful parts of the distribution, separated into parts using σ .




13

The local adaptive cumultative histogram features H i (σ ) are defined as follows: −( σ  + 1)

∑

H L (σ ) =

H n ( p) ,

(13)

H n ( p) ,

(14)

H n ( p) .

(15)

p = −∞ +σ 

H M (σ ) =

∑

p = −σ 

H R (σ ) =

∞

∑

p σ  + 1 =

Since the Intaglio printing technique is closely related to the Wavelet Transform [5], the H i (σ ) features are obviously connected to Intaglio and therefore, they represent appropriate measures which can serve as features for the pattern recognition process. Furthermore, the additional features describe the local distributions, whereas the existing statistical moments describe the shape of the distribution. It implies that the additional LACH features are complementary to the existing statistical features. Table 4 – Lilliefors test applied to LACH features – GENUINE


HL

HM

HR

not normal normal normal



Table 5 – Lilliefors test applied to LACH features – FORGERY


HL

HM

HR


not normal not normal normal

not normal not normal not normal

We intuitively interprete this fact that LACH features might be suitable for LDA (cf. Table 4 and Table 5). The LDA solution is recalculated taking the LACH features into accout. Obviously, the increasing of the value ( µ (G ) − µ ( F ) )

2

and decreasing of σ 2 (G ) and σ 2 ( F ) in the LDA solution implies an improvement of the separability between different classes. In the above mentioned example we achieve for the three features the following Rayleigh coefficients: For the top region 2

Dtop = 27.52 with ( µ (G ) − µ ( F ) ) = 1.1173 , σ 2 (G ) = 0.0213 , σ 2 ( F ) = 0.0193 ; for the middle region •

2

Dmiddle = 32.67 with ( µ (G ) − µ ( F ) ) = 4.28 , σ 2 (G ) = 0.043 , σ 2 ( F ) = 0.088 ; for the bottom region

•

•

2

Dbottom = 7.93 with ( µ (G ) − µ ( F ) ) = 0.5287 , σ 2 (G ) = 0.0153 , σ 2 ( F ) = 0.057 .

The previous and now calculated values with increased ( µ (G ) − µ ( F ) ) and decreased σ 2 (G ) and σ 2 ( F ) show that in 2

fact the new LDA solution with additional features improves the separability of the classes. In Figure 11 the combination of the three features with the biggest discriminant (we call them strongest features) is presented. In general, the LDA solution for six features cannot be worse than the LDA solution for three features.




14

Figure 11 – Feature spaces of genuine and forged objects for the top, middle and bottom ROIs for the strongest three features are presented. In all cases the feature space is normalized to [0, 1] for the class GENUINE. The linear separability is improved.

It is intuitive, that using the additional features an accurate linear classification is possible in the top and middle regions. The accuracy is improved, but not perfect, in the bottom region. However, in this region it is still not possible to separate the classes linearly. In this case the decision boundary for linear classification in this ROI is selected so that the number of the false classified objects is minimal. It has to be noted, that in Figure 11 the correlation between the features seems to be stronger than it actually is, due to the chosen perspectives of the figures. On the other hand, as long as no perfect correlation occurs, a feature adds new information and therefore is not useless for classification. Strongercorrelated features can lead to better separability than weak-correlated ones. Figure 12 visualizes this effect.

Figure 12 – Schematic illustration showing the connection of correlation and fitness for classification. Stronger-correlated feature (right) can be more suitable for the classification than weak-correlated ones (left).

The general concept is based on the fact that from different features (six to date) a combination of at least three features will be composed. As a measure for composition the Rayleigh coefficient is used. In the current state of the software, for the sake of simplicity the decision for GENUINE is taken, if all three ROIs are classified as genuine. Similarly, the decision for FORGERY is taken, if all three ROIs are classified as counterfeited. In the near future, the possibility for the improvement of the linear separability in the bottom region will be studied. Further, a weighted classification with weights depending on discriminants will be tested, if better results can be achieved.




15

4. IMPLEMENTATION AND RESULTS A smartphone is designed for the usage in lightish environments to generate images with an adequate Signal-tonoise-ratio. As explained before the hardware is not optimised for industrial image processing as such. Furthermore, the user has to position and to stabilise the smartphone above the banknote in a specific distance (cf. Figure 13). The illumination (LED) has to be switched on because otherwise the captured image will be too dark. On the other hand smartphones impress with their resolution.

Fig. 13 – Comparison between a LabQMD device (left) and a smartphone (right) in use.

In comparison, the KBA-NotaSys LabQMD (Laboratory Quality Measurement Device) device has some advantages though the resolution is lower. The illumination and camera unit offers a defined distance from the optics to the banknote, and no scattered light is influencing the image capture (cf. Figure 13). 4.1 Implementation As the automated image capture process includes also image sharpening and compression, we face the challenge to develop a concept to store the free-hand captured data and process them accordingly. In accordance with Android Mobile OS 2.3.4 it should be possible, using the configuration-JAVA-class Camera, to transfer the data to a specific application (Mobile SoI). In the application the data should be stored as a byte-stream similar to the procedure in the LabQMD. There is one exclusion: If the uncompressed image is too large for the program memory then the system returns an exception error. Obviously, the interface is neither implemented correctly nor adequately tested. This effect was approved by different user and developer groups [44]. This was understood by rooting a telephone device and defining enough memory for a specific application. By using the application programming interface (API) in a default path it is not possible to gain feasible images. Therefore, the preview image in the NV21 / YCrCb format was used. As explained, the Y-channel seems to be uncompressed and of lower resolution (approx. 0.9 Megapixel), and is therefore usable for image processing applications. The implementation strategy is different from the LabQMD device. As it is intended to use the resourcelimited hardware optimally, the pre-processing part of the camera module generates in one step a greyscale image in the preferred size of 400 × 400 pixel for the top, middle and bottom region, indicated in Figure 7. It has to be pointed out that a Shift-invariant Wavelet Transform and the classifiers have to programmed and implemented in JAVA. These algorithms do not belong to any class. The processing time for the Mobile SoI algorithm is 1.2 seconds for a Sony Ericsson Xperia arc with Android 2.3.4. This time includes the image capture, the SWT, statistical feature generation, classification (three classifiers), and graphical result displaying. Using mobile devices, one of the main topics is based on the integrated cameras and optics. We have intensively researched the quality of different cameras and the associated optics (cf. Fig. 14). It can be stated that nearly all cameras, when controlled properly by the application software, can be used for banknote authentication. In Figure 13 example images are shown. Different mobile telephone resolutions are compared with a standard image processing camera. However, to date a Sony Ericsson Xperia arc is used.




16

Fig.14 – From left to right – Standard KBA-NotaSys LabQMD resolution (0.31 Megapixel); iPhone 4 (5 Megapixel); HTC Desire HD (8 Megapixel); Sony Ericsson Xperia arc (8 Megapixel).

4.2 Results The test results are based on a Sony Ericsson Xperia arc. In the next subsection some images are presented which highlight the demonstrator program. Based on the Figure 7 a preview image with 1280 × 720 is used. The smartphone’s pre-processing generates the image and captures the three 400 × 400 sub-images. In the next step the user has to position the smartphone camera above the banknote with the help of the overlay mask; cf. Figure 15.

Fig. 15 – The banknote specific mask is positioned above the banknote and the smartphone is triggered by hand.

In the upper right edge of the image three circles are visible. To date, for Intaglio detection at least two circles must be green and one marked nil. As well as for a counterfeit detection two circles must be red and one must be nil. All other combinations indicate a non-allocatable banknote. Especially when nil is outputted different effects can be assumed: the overlay mask is not in the right position (main case); the image is blurred; the classifier can not distinguish between Intaglio and counterfeit (rare case). Table 6 – Context information of the three circles in the upper right of the smartphone’s touch display. For Intaglio detection two of three must be circles must be green and one can be nil. As well as for a counterfeit detection two circles must be red, one nil. All other combinations indicate a non-allocatable banknote, e.g.: Circles from top to bottom Top region (1. circle) Middle region (2. circle) Bottom region (3. circle)

green ×

red ×

nil ×

The classifiers are trained with 66 images per each class which is not much for a complex image processing system. The results are visualised in Figure 16. It has to be pointed out here that the first tests were promising. Besides genuine notes, so called High-Quality Forgeries (HQF) and Low-Quality Forgeries (LQF) were tested. The HQF are generated in Offset printing using the original digital data, whereas the LQF are printed in Offset using scanned (10,000 dpi) data. Table 7 presents the data which are randomly generated by three persons and four different smartphones of the same type Sony Ericsson Xperia arc per class (genuine, HQF, LQF) 198 banknotes are used.




17

Fig. 16 – Detection of genuine and counterfeited Intaglio print with a smartphone application.

It is noticeable that in none of the cases a counterfeited banknote was classified as a genuine banknote and vice versa. The following information for the data set is determined: True Positive Rate (TPR): 80.3 %; mean True Negative Rate (TNR): 96.7%; mean False Positive Rate (FPR): 0 %; False Negative Rate: 0 %; mean Nil Rate: 8.77 %. The word Positive is used in the sense of GENUINE, whereas the word Negative is used in the sense of FORGERY. The Nil Rate is mainly caused by positioning and blur effects. Table 7 – Test results of three bank note classes. Classes / detected as Genuine High-Quality Forgery Low-Quality Forgery

Genuine [%] 80.3 0 0

Nil [%] 19.7 2.5 4.1

Counterfeit [%] 0 97.5 95.9

Based on the actual results it can be stated that there is potential for optimisation, just to name a few topics: Stabilisation with the internal gyrometer; image capture with the maximum speed which is to date approx. 25 Hz and taking out the most stable image in terms of illumination and blur; for security reasons (code reverse engineering) it is appropriate to generate a distributed application software which partly runs in a secure Cloud environment. In the near future it is expected that an Android version will be available which will allow the access to RAW images. Furthermore, other smartphones which are in general able to handle a suchlike application will be researched regarding their processing capabilities.

5. CONCLUSION AND OUTLOOK The work shows that smartphones are able to handle complex image processing algorithms for banknote authentication. Furthermore, an adapted version of the well-known Sound-of-Intaglio concept (Mobile SoI) could be implemented on a Sony Ericsson Xperia arc smartphone. Yet it has to be pointed out strictly that a mobile device as such is not an industrial product for harsh environments. The lesson which is learned is the following: Many optimisations have to be implemented to get stable results. Furthermore, the Mobile Operating Systems have some restrictions which can not be neglected. One general drawback is based on the fact that some modules are closed source, so that it is not possible to get a direct hardware access to the devices. However, there is potential for optimisation, just to name a few topics: Stabilisation with the internal gyrometer; image capture with the maximum speed which is to date approx. 25 Hz and takes out the most stable image in terms of illumination and blur; for security reasons (code reverse engineering) it is appropriate to generate a distributed application software which partly runs in a secure Cloud environment. In general such an application is interesting for all visually impaired persons when the application has additional functionality such as a speech processing output.




18

ACKNOWLEDGEMENTS This work was mainly financed by KBA-NotaSys S.A., Lausanne, Switzerland. The authors would like to thank Prof. Stefan Heiss from the Institute Industrial IT for valuable hints regarding smartphones, and Jürg Hofmann from KBANotaSys for valuable input and information.

REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25]

Glock, S.; Gillich, E; Schaede, J. G.; Lohweg V., “Feature Extraction Algorithm for Banknote Textures based on Incomplete Shift Invariant Wavelet Packet Transform”, Proceedings of the 31st DAGM Symposium on Pattern Recognition, Lecture Notes on Computer Science, vol. 5748, pp. 422-431, Springer, 2009. de Heij, H.A.M., “Public feedback for better banknote design”, IS&T/SPIE’s International Symposium on Electronic Imaging, Optical Security and Counterfeit Deterrence Techniques VI, San José, California, USA , Proceedings of SPIE vol. 6075, pp. 1-40, 2006. de Heij, H.A.M., “Public Feedback for better Banknotes Design 2”, DNB Occasional Studies, vol. 5, no. 2, De Nederlandsche Bank NV, Amsterdam, 2007. Schaede, J. G.; Lohweg, V., “The Mechanisms of Human Recognition as a Guideline For Security Feature Development”, IS&T/SPIE 18th Annual Symposium on Electronic Imaging - Optical Security and Counterfeit Deterrence Techniques VI, vol. 6075, SPIE, Feb 2006. Lohweg, V.; Schaede, J. G., “Document Production and Verification by Optimization of Feature Platform Exploitation. In: Optical Document Security - The Conference on Optical Security and Counterfeit Detection”, San Francisco, CA, USA, January 20-22, 2010 Jan 2010. de Heij, H.A.M., “Banknote Design for the visually impaired”, DNB Occasional Studies, vol. 7, no. 2, De Nederlandsche Bank NV, Amsterdam, 2009. Moore, G. E., Cramming more components onto integrated circuits. In: Electronics. 19, No. 3, pp. 114–117, 1965. Gartner, Gartner Says Sales of Mobile Devices Grew 5.6 Percent in Third Quarter of 2011; Smartphone Sales Increased 42 Percent, http://www.gartner.com/it/page.jsp?id=1848514, retrieved 2011-11-24. Strategy Analytics: http://www.marketwatch.com/story/strategy-analytics-china-overtakes-united-states-asworlds-largest-smartphone-market-in-q3-2011-2011-11-23; retrieved 2011-11-24. Tse, D.; Viswanath, P., Fundamentals of Wireless Communication, Cambridge University Press, 2005. Schwarz, M., Mobile Wireless Communications, Cambridge University Press, 2005. Open Handset Alliance, http://www.openhandsetalliance.com/, retrieved 2011-11-24. Morris, B., The Symbian OS architecture sourcebook: design and evolution of a mobile phone OS, John Wiley & Sons. 2007. Mark, D.; LaMarche, J, Beginning iPhone 3 Development: Exploring the iPhone SDK (1st ed.), Apress, 2009. Geller, J. S., “RIM’s first QNX phone revealed: BlackBerry Colt to launch in Q1 2012”, http://www.bgr.com/2011/08/08/rims-first-qnx-phone-revealed-blackberry-colt-to-launch-in-q1-2012/, retrieved 2011-11-24. Microsoft Mobile 7, http://www.microsoft.com/en-gb/family/default.aspx, retrieved 2011-11-24. Bada Official Site, http://www.bada.com/whatisbada/, retrieved 2011-11-24. Gartner, Gartner Says Worldwide Mobile Device Sales to End Users Reached 1.6 Billion Units in 2010; Smartphone Sales Grew 72 Percent in 2010 (Table 2), http://www.gartner.com/it/page.jsp?id=1543014, retrieved 2011-11-24. Reisinger, D., “Android smartphone share more than triples iOS in Q3”, CNET, http://news.cnet.com/830113506_3-57324963-17/android-smartphone-share-more-than-triples-ios-in-q3/, retrieved 2011-11-24. Fake Currency Doctors: http://voicendata.ciol.com/content/service_provider/110020318.asp, 2010-02-03, retrieved 2011-11-24. Illés, L, iValuta, http://itunes.apple.com/us/app/ivaluta/id327705750?mt=8, retrieved 2011-11-27. Macsoftex, All Dollars, http://itunes.apple.com/tw/app/all-dollars/id341552027?mt=8, retrieved 2011-11-27. Girardi, M., EuroCheck, http://itunes.apple.com/us/app/eurocheck/id358981465?mt=8, retrieved 2011-11-27. Zchut, A., Banknote or Not?, http://itunes.apple.com/de/app/banknote-or-not/id329677699?mt=8, retrieved 2011-11-27. Phoum, Lib: Bill Sound Scanner, http://itunes.apple.com/us/app/bill-sound-scanner/id330462260?mt=8, retrieved 2011-11-27.



Mobile Devices for Banknote Authentication – Is it Possible? [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42]

[43] [44] [45] [46] [47] [48] [49] [50] [51]

19

Caputo, M., MoneyScan Pro, http://itunes.apple.com/us/app/money-scan-pro/id398958662?mt=8, retrieved 2011-11-27. Santiniki: Counterfeit, http://itunes.apple.com/us/app/counterfeit/id391330501?mt=8, retrieved 2011-11-27. Bureau of Engraving and Printing, EyeNote, http://itunes.apple.com/us/app/eyenote/id405336354?mt=8, retrieved 2011-11-27. Ipplex, LookTel Money Reader, http://itunes.apple.com/us/app/looktel-money-reader/id417476558?mt=8, retrieved 2011-11-27. Mallat, S. G., “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, No. 7, pp. 674-693, 1989. Daubechies, I., “Ten Lectures on Wavelets”, CBMS-NSF Regional Conference Series in Applied Mathematics 61, SIAM (Society for Industrial and Applied Mathematics), 2nd edition, 1992. Burrus, S. C.; Gopinath, R. A.; Guo, H., Introduction to Wavelets and Wavelet Transforms: A Primer, Prentice-Hall, Inc., 1998. Mallat, S. G., A Wavelet Tour of Signal Processing, Academic Press, 2nd edition, 1999. Walnut, D. F., An Introduction to Wavelet Analysis, Birkhäuser Boston, 2nd edition, 2004. Willmore, B. et al., “The Berkeley Wavelet Transform: A Biologically Inspired Orthogonal Wavelet Transform”, Neural computation, vol. 20, no. 6, pp. 1537-1564, June 2008. Pesquet, J. C.; Krim, H.; Carfantan, H., “Time-invariant orthonormal wavelet representations”, in: IEEE transactions on signal processing 8, pp. 1964-1970, 1996. Fowler, J. E., “The redundant discrete wavelet transform and additive noise”, in: IEEE Signal Processing Letters 9, pp. 629-632, 2005. Gillich, E., Lohweg, V.: “Banknote authentication” in: 1. Jahreskolloquium “Bildverarbeitung in der Automation”, ISBN 978-3-9814062-0-7. Centrum Industrial IT, Lemgo 2010. Schölkopf, B.; Smola, A. J., Learning with kernels. Support vector machines, regularization,optimization, and beyond, MIT Press (Adaptive computation and machine learning), Cambridge 2002. van Renesse, R.L., “Optical Inspection techniques for Security Instrumentation”, IS&T/SPIE’s Symposium on Electronic Imaging, Optical Security and Counterfeit Deterrence Techniques I, San José, California, USA, Proceedings of SPIE, vol. 2659, pp. 159-167, 1996. Treinen, H.; Seidensticker S., Public reception of double side Intaglio printed banknotes compared with single side Intaglio and Offset counterfeited banknotes, Wissenschaftszentrum NRW, Institut Arbeit und Technik, Düsseldorf, Germany, 2004. Dyck, W. Türke, T.; Schaede, J. G.; Lohweg, V., “A New Concept on Quality Inspection and Machine Conditioning for Security Prints”, Optical Document Security - The 2008 Conference on Optical Security and Counterfeit Deterrence; Reconnaissance International Publishers and Consultants, San Francisco, CA, USA, Jan 2008. Choi, E.; Lee, J.; Yoon, J., “Feature Extraction for Bank Note Classification Using Wavelet Transform”, IEEE, The 18th International Conference on Pattern Recognition (ICPR'06), vol. 2, pp. 934 - 937, 2006. Android Developers, http://developer.android.com/reference/android/hardware/Camera.html, retrieved 201111-27. Poynton, C., Digital Video and HDTV Algorithms and Interfaces, Morgan Kaufmann Publishers, San Francisco 2003. Fischer, R., “The use of multiple measurements in taxonomic problems”, in: Annals of Eugenics, 7, pp. 179188, 1936. Duda, R. O.; Hart, P. E., Pattern classification, John Wiley, New York 2000. Lilliefors, H., “On the Kolmogorov–Smirnov test for normality with mean and variance unknown”, Journal of the American Statistical Association, Vol. 62., pp. 399–402, 1967. Massey, F. J., “The Kolmogorov-Smirnov Test for Goodness of Fit”, Journal of the American Statistical Association, 46, pp. 68-78, 1951. Nikulin, M. S., “Chi-squared test for normality”, in: Proceedings of the International Vilnius Conference on Probability Theory and Mathematical Statistics, vol.2, pp. 119–122, 1973. David, F. N.; Johnson, N. L., “The Probability Integral Transformation When Parameters Are Estimated From the Sample”, Biometrika, vol. 35, pp. 182-90, 1948.

