Mosharaka International Conference on Communications, Computers and Applications
CRYPTO KEY GENERATION BASED ON SIGNATURE BIOMETRIC USING GABOR FEATURES SELECTION M. S. AlTarawneh1 and Bassam Al-Mahadeen2 1: Faculty of Engineering, Computer Engineering Department, Mutah University Mutah, Karak, 61710, Jordan, e-mail
[email protected] 2: College of Sciences, Mathematics & Department, Tafila Technical University Tafila, 66110,Jordan, e-mail
[email protected]
ABSTRACT This paper presents a direct crypto key generation method form signature biometric based on Gabor feature selection. The proposed method outputs secure entropy keys, which could be used as high watermarking source for signature biometric itself as well as digital electronic signature that hide e-documents in electronic open system. Simulated results have demonstrated that generated Crypto Key addresses a replacement of a biometric signature representation into digital symmetric key; where the generated features vector key is used to sign e’s sources, i.e. e-banking, e-insurance, e-health care, and e-government documents. Key words: - Biometric, Signature, Features, Gabor vector, Cryptography and Security.
I. INTRODUCTION Introduction In the last decade information technology fields have become very essential parts of our daily life, it tackle private information that must be secure in a sense of personal, public and governmental related cases. Human daily work widespread used of electronic representation, such as e-commerce, e-banking, evoting and e-government. Since there are billion of transactions in this electronic environment, security becomes a critical problem that must be solved by new reliable and robust authentication or cryptographic techniques involving user-based behavioural and physiological characteristics known as biometrics. Biometric authentication system offers greater security and convenience than traditional personal verification systems. Biometrics is about measuring unique personal features [1]. Biometric signature is one of behavioural handwriting process. The signature of each individual is assumed to be unique because of the complex nature of the writing process, the relatively large number of elements and the variability over writers in the forms of these elements. Signature is one of the most widely used authentication methods due to its acceptance in government, legal, financial and commercial
transactions [2]. A biometric signature was used as a source of cryptographic platform because of usability, privacy, low cost, universality and fair spoof proofing, these factors were concluded on following advantages [3]: No forgery of biometric signatures is possible, as it involves distinct writing styles of different individuals. Encryption and decryption algorithms are used to create the templates for different user signatures and these algorithms are difficult to break by intruders. Easy to employ and low-cost technology, with limited requirements of special hardware. Eliminates the headache of remembering personal identification number (PIN) and passwords to access different systems. Signature based identification is a process already familiar to each and every person and thus, biometric signature recognition is easy to understand. Biometric signature is the subject of interest in this research; it deals with the process of verifying the written signature patterns of human individuals. For the first time it will tackle the offline signature and later in future work On-line data acquisition will be interest of research, signatures registration, preprocessing, storage, feature extraction algorithm and off-line key generation are investigated in this research. There are basically two types of signature representation methods, on-line and off-line signature representation. Off-line method identifies signatures using an image processing procedure whereby the user is supposed to have written down completely the signature onto a template that is later captured by camera or scanner to be processed. On-line signature involved the capturing of dynamic signature signals such as pressure of pen tips, time duration of whole signature and velocity along signature path. To overcome the integration of biometric signature into cryptosystem, a number of approaches were introduced in the literatures. The offline approaches can only operate on static image data; therefore they often try to compare global features like size of the signature or similarities of the contour [4, 5]. Some approaches worked on image
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Mosharaka International Conference on Communications, Computers and Applications
transformation and density models of the signatures like [6, 7]. Off-line methods do not require special acquisition hardware, just a pen and a paper, they are therefore less invasive and more user friendly. In the past decade a bunch of solutions has been introduced, to overcome the limitations of off-line signature verification and to compensate for the loss of accuracy. Most of these methods have one in common: they deliver acceptable results but they have problems improving them. This is the main reason, why static signature analysis is still in focus of many researchers. None of literature tackled enhancement feature extractions such as filtration technique this is why this research introduced Gabor vector feature selection as based of key generation. The rest of this paper is structured as follows: the first part describes the architecture of the proposed approach followed by a description on the signature pre and post-processing techniques. Consequently, vector feature extraction using Gabor selection, key generation and evaluation of proposed approach is carried out towards the end of paper with conclusion and direction for future study is finally outlined.
all features in each filtered image. These features capture the local characteristics of signature ridges. The key Code scheme of feature extraction tessellates the region of interest in cropped image, i.e. cropped images into [16X16], [32X32] and [64X64] surrounding the image reference point, where the scheme is divided into two stages: preprocessing and feature extraction stages Figure 2.
Figure 2. Signature Vector Features scheme
II.1.
Sigature Image Preprocessing
The preprocessing stage contains three main steps: image resizing into 128x28 pixels, cleaning the image using morphological operation, then rotate the signature against its curve computation figure 3.
II. PROPOSED APPROACH The key generation approach involves a number of stages as shown in Figure 1, where the first stage is pre-processing of signature image on base of image resizing, noise removing, signature rotating and boundary boxing. The out put of previous stage will be cleaned, thinned signature image, it will be input of post-processing stage where the thinned image will be passed through image processing enhancement technique, image cropping and sectornization into 3 options [16X16], [32X32] and [64X64] surrounding the image center point, and finally Gabor filtration.
a- original signature
b- resized signature
c- cleaned signature
d- rotated signature
Figure 3 Signature image pre-processing steps
Figure 1: Proposed approach block diagram.
A key Code is composed of an ordered enumeration of the feature extracted from the local information contained in each image sector in cropped images. A feature vector is the collection of
II.2.
Thinned signature Postprocessing
1) Signature enhancement and cropping The performance of signature feature extraction and key code generation depend heavily upon the quality of the input signature image, therefore this stage
MIC-CCA2009
Mosharaka International Conference on Communications, Computers and Applications
starts with applying short fast furrier transformation. Signature image enhancement algorithm based on contextual filtering in the Fourier domain is used in this step. The used algorithm is able to simultaneously estimate the local signature orientation and frequency information using short time Fourier analysis. The algorithm is also able to successfully segment the signature images [8]. Then the image is cropped into three options of cropping images centred on pseudo-centre point of enhanced signature figure 4.
Figure 5: Sectorization and normalization enhanced signature
a- enhanced signature center
b- cropped signature
Figure 4: Signature enhancement and cropping
2) Sectorization and Normalization The cropped enhanced signature image is divided into 5 centronic bands centred around the pseudocentre point figure 5. The image region of interest is the collection of all the sectors such that:
Si = {(x , y ) b(Ti + 1) ≤ r 〈 b(Ti + 2),
θi ≤ θ ≤ θi +1 ,1 ≤ x ≤ N ,1 ≤ y ≤ M} (1)
And all the pixels outside of the sector map is considered to be one giant sector. This will yield in an image that is more uniform. The following is the equation which we used for normalization of each pixel. We used a constant mean M0 and variance V0 of 100. i is the sector number, Mi is the mean of the sector, and Vi is the variance of the sector. ( ) 2 M + V0 I x , y − M i 0 Vi if I(x , y )〉 M i N i (x , y ) = V0 I(x , y ) − M i2 M0 − (2) otherwise Vi
II.3.
where
Ti = i div k , θi = i mod k × 2 π , k
(
) (
)
(x − x c )2 + (y − y c )2 , θ = tan −1 ((y − y c ) / (x − x c )) r=
In the sectorization step, each sector will capture information corresponding to the Gabor filter which will be used in Gabor filter banking in feature extraction stage. Sectorization is used for normalization purposes too. Each sector is individually normalized to a constant mean and variance to eliminate variations in darkness in the signature pattern, due to scanning noise and pressure variations.
(
)
(
)
Feature Extraction
1) Gabor Filtering We then pass the normalized image through a bank of Gabor filters. Each filter is performed by producing a 33x33 filter image for 6 angles (0, π/6, π/3, π/2, 2π/3 and 5π/6), and convolving it with the signature image. The purpose of applying Gabor filters is to remove noise while preserving signature line structures and providing information contained in a particular direction in the image. The sectorization will then detect the presence of ridge lines in that direction. The Gabor filter also has an odd height and width to maintain its peak center point. The following is the definition of the Gabor filter [9]:
G ( x , y, f , θ) = e
2
2
x
y
1 x' y' − { 2 + 2} 2 σ ' σ '
⋅ cos(2πfx ' )
(3)
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Mosharaka International Conference on Communications, Computers and Applications
where
III. SIMULATION AND RESULTS
x = (x cosθ + y sin θ), y = (−x sin θ + y cosθ) '
'
are rotated coordinates, 2)Feature Vector After we get the 6 filtered images, we calculate the variance of the pixel values in each sector. This will tell us the concentration of signature lines. A higher variance in a sector means that the ridge lines in that image were going in the same direction as is the Gabor filter. A low variance indicates that the ridges were not, so the filtering smoothed them out. The following is the equation for variance calculation. Siθ are the pixel values in the ith sector after a Gabor filter with angle θ has been applied. Piθ is the mean of the pixel values. Ki is the number of pixels in the ith sector.
Viθ =
∑ (Siθ (x , y ) − Piθ )2
(4)
Ki The result will be three vector features for cropped images. A concatenation value of these vectors will formulate final used feature 5 [10].
V = V1 V2 V3
(5) Where this vector will be used as true key code in binary representation with a length 140 bit figure 6 for author signature image, figure 7 key code plotting representation in x,y coordination GEN_Key=000011000101010100010010000110001 0001011011000011000011000110010011111010000 1100001001011111000001000011000101100011100 001110101110110101001 Figure 6: Key code of author signature image.
Proposed approach implemented on the GPDS300signature database [11], a public domain database of off line signature. It contains data from 300 individuals: 24 genuine signatures for each individual, plus 30 forgeries of his/her signature. The 24 genuine specimens of each signer were collected in a single day writing sessions. The forgeries were produced from the static image of the genuine signature. Each forger was allowed to practice the signature for as long as s/he wishes. Each forger imitated 3 signatures of 5 signers in a single day writing session. The genuine signatures shown to each forger are chosen randomly from the 24 genuine ones. Therefore for each genuine signature there are 30 skilled forgeries made by 10 forgers from 10 different genuine specimens. The signatures are in "bmp" format, in black and white and 300 dpi. Results show that generated key completely depends on signature image quality assurance. The length of extracted key is 140 bit, the entropy of key and uniqueness is 100%
IV. CONCLUSION A signature-based key generation approach has been proposed, using Gabor filter banks applied to the signature enhanced images to get cryptographic vector features. An analysis of the suitability of different signature parameters has also been conducted. Preliminary experiments reveal the feasibility of crypto-biometric systems based on offline signature. Results reveal high false rejection but considerably low false acceptance to decode the secret key code. Therefore work is currently being done in enhancing automatic rotated images. In the future, a research will be directed to study the fusion of global and local features in signature-based cryptobiometric systems, and the application of signaturebased cryptosystems to new scenarios of eenvironment era.
REFERENCES
Figure 7: key code plotting representation in x,y cordination
[1] A. K. Jain, A. Ross, and S. Prabhakar, "An Introduction to Biometric Recognition," IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Image- and VideoBased Biometrics, vol. 14, pp. 4-20, 2004. [2] M. C. Fairhurst, "Signature verification revisited: promoting practical exploitation of biometric technology," IEE Electronics and
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Communication Engineering, vol. 9, pp. 273– 280, December 1997. [3] L. Ballard, D. Lopresti, and F.Monrose, "Forgery quality and its implications for biometric security," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Special Issue),, vol. 37, pp. 1107–1118. [4] Martinez L.E, Travieso C.M, Alonso J.B, and Ferrer M.A, "Parametrization of a Forgery handwritten signature verification system using SVM," IEEE, vol. 2, pp. 193-196, 2004. [5] Miguel A. Ferrer, Jesus B. Alonso, and C. M. Travieso., "Offline Geometric Parameters for Automatic Signature Verification Using FixedPoint Arithmetic.," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 993-997, 2005. [6] H. A. Sofien Touj and Najoua Ben Amara, "Global feature extraction of offline arabic handwriting,," IEEE SMC, vol. 2, 2003. [7] J. Mahmud and Chowdhury Mofizur Rahman, "On the Power of Feature Analyzer for Signature Verification.," presented at Proceedings of the Digital Imaging Computing: Techniques and Applications (DICTA 2005), 2005. [8] S. Chikkerur, A. N. Cartwright, and V. Govindaraju, "Fingerprint enhancement using STFT analysis," Pattern Recogn., vol. 40, pp. 198-211, 2007. [9] A. K. Jain, S. Prabhakar, L. Hong, and S. Pankanti, " Filterbank-based Fingerprint Matching," IEEE Transactions on Image Processing, vol. 9, pp. 846-859, 2000. [10] M.S. ALTARAWNEH, L.C.KHOR, W.L.WOO, and S. S. DLAY, "Crypto Key Generation using Slicing Window Algorithm," presented at 5th International Conference on CSNDSP, Greece, 2006. [11] http://www.gpds.ulpgc.es/download/index.htm.
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