Multibiometric Systems Based Verification Technique

European Journal of Scientific Research ISSN 1450-216X Vol.34 No.2 (2009), pp.260-270 © EuroJournals Publishing, Inc. 2009 http://www.eurojournals.com/ejsr.htm

Multibiometric Systems Based Verification Technique Farhat Anwar Faculty of Engineering, Department of ECE International Islamic University Malaysia P.O. Box 10, 50728, Kuala Lumpur, Malaysia E-mail: [email protected] Tel: (603) 6196 4578; Fax: (603) 6196 4488 Md. Arafatur Rahman Faculty of Engineering, Department of ECE International Islamic University Malaysia P.O. Box 10, 50728, Kuala Lumpur, Malaysia E-mail: [email protected] Tel: (603) 6196 4578; Fax: (603) 6196 4488 Md. Saiful Azad Faculty of Engineering, Department of ECE International Islamic University Malaysia P.O. Box 10, 50728, Kuala Lumpur, Malaysia E-mail: [email protected] Tel: (603) 6196 4578; Fax: (603) 6196 4488 Abstract Multibiometric systems represent the fusion of two or more unimodal biometric systems. Such systems are expected to be more reliable due to the presence of multiple independent pieces of evidence. Since security is one of the important concerns of twenty first century, multibiometric systems can play an important role in ensuring security. In this paper, a multibiometric system is proposed for human verification i.e. authenticating the identity of an individual. The geometrical dimensions of a human hand, such as hand geometry, finger stripe geometry or palm geometry, contain information that is capable of verifying an individual. Among them, hand geometry based verification is the oldest implemented biometric type (debuting in the market in the late 1980s) and finger stripe is the most recent biometric type proposed. The proposed multibiometric system uses both hand and finger stripe geometry for verification. Artificial Neural Network (ANN) is applied for feature learning and verification process. At the end of the paper, results based on 200 individuals and comparison between proposed method and published alternative methods are demonstrated. The experimental results exhibit that the proposed method outperforms the existing methods.

Multibiometric Systems Based Verification Technique 261 Keywords: Multibiometrics, Human Verification, Artificial Neural Network, Back propagation algorithm.

1. Introduction Personal identification is ubiquitous in our daily lives. For example, we often have to prove our identity for getting access to bank account, entering a protected site, drawing cash from an ATM, logging in to a computer, and so on. Conventionally, we identify ourselves and gain access by physically carrying passports, keys, access cards or by remembering passwords, secret codes, and personal identification numbers (PINs). Unfortunately, passport, keys, access cards can be lost, duplicated, stolen, or forgotten; and password, secret codes, and personal identification numbers (PINs) can easily be forgotten, compromised, shared, or observed. Such loopholes or deficiencies of conventional personal identification techniques have caused major problems to all concerned. For example, hackers often disrupt computer networks, credit card fraud is estimated at billions dollars per year worldwide. The cost of forgotten passwords is high and accounts for 40%-80% of all the IT help desk calls and resetting the forgotten or compromised passwords costs as much as US$ 340/user/year (Andrijchuk, et al 2005). Therefore, robust, reliable, and foolproof personal identification solutions must be sought in order to address the deficiencies of conventional techniques, something that could verify that some one is physically the person he/she claims to be. A biometric is a unique, measurable characteristic or trait of a human being for automatically recognizing or verifying identity. By using a biometric identification, the individual verification can be done by doing the statistical analysis of biological characteristic. This measurable characteristic can be physical, e.g. eye, face, finger image and hand, or behavioral, e.g. signature and typing rhythm. Besides bolstering security, biometric systems also enhance user convenience by alleviating the need to design and remember multiple complex passwords. No wonder large scale systems have been deployed in such diverse applications as US-VISIT and entry to Disney Park, Orlando. The revenues for the global biometric recognition market are papered to grow from about US $2.1 billion in 2006 to US $5.7 billion in 2010 (Jain, 2007). In spite of the fact that automatic biometric recognition systems based on fingerprints (called AFIS) have been used by law enforcement agencies worldwide for over 40 years, biometric recognition continues to remain a very difficult pattern recognition problem. A biometric system has to contend with problems related to noisy images (failure to enroll), lack of distinctiveness (finite error rate), large intra-class variations (false reject), and spoof attacks (system security). Therefore, a proper system design is needed to verify a person quickly and automatically (Uludag et al 2004). In this paper, a multibiometric system is proposed for human verification i.e. authenticating the identity of an individual. The proposed multibiometric system uses both hand and finger stripe geometry for verification process. Artificial Neural Network (ANN) is applied for feature learning and verification. The rest of the paper is organized as follows. Section 2 highlights related previous research. Section 3 briefly explains multibiometric systems. The methodology of the proposed hand geometry based verification system is described in Section 4. Section 5 and 6 describe the training phase and verification phase respectively. Section 7 discusses the experimental results and shows the comparison with other existing hand geometry based algorithm. Finally, conclusion is drawn in section 8 which also includes suggestions regarding future work.

2. Previous Research Person recognition is not a new concept. Ancient Egyptians used body measurements to classify and identify people. Hand geometry systems have the oldest implementation history of all biometric types. In the late 1960’s, Robert P. Miller issued patents from U. S. Patent office for a device that measures hand characteristics and records unique features for comparison and identity (ID) verification (Miller,

262 Farhat Anwar, Md. Arafatur Rahman and Md. Saiful Azad 1971). In the year 1985, David Sidlauskas developed and patented one hand geometry concept (United States Patent and Trademark, 1988) and the first commercial hand geometry recognition systems became available in 1986 (IR Recognition Systems). Thus the voyage of hand geometry commenced and still gaining attention from the researchers. However, finger stripe geometry (Rahman et al, 2007) is considerably a new biometric type. Although both of them use geometric dimension of human hand, they have various significant features which separate them from each other. A biometric authentication system can be categorized into two main functionalities (Jain et al, 2006), (Niennattrakul and Ratanamahatana , 2007) – identification and verification. An identification system collects an image from an input sensor, extracts its features, and queries a database to find out the best match. If the input image is different from the best retrieved pattern, the system rejects, otherwise, it accepts and identifies the input’s identity. To uniquely identify an individual based on biometric data, some characteristics are desirable. They need to be highly unique to each individual, easily obtainable, time invariant, and able to be acquired as non-intrusively as possible (You et al, 2002), (Shen and Tan, 1999). Nonetheless, a verification system requires a user identity as an additional input. Instead of querying to the database for the best matched pattern, the verification system queries on only the claimed user’s own patterns. Matching only on much smaller set of templates generally makes the verification system much more accurate and simpler than the identification system (Niennattrakul and Ratanamahatana , 2007). Consequently, for verification, unique biometric system is not necessary such as, finger print, iris etc. However, in this scheme, it is desirable to have such a biometric system from which data can be easily extracted and collected. Moreover, suitability of a particular biometric to a specific application depends upon several factors such as, comfort, accuracy, acceptability and cost. Among these factors, the user acceptability seems to be the most significant (Kumar and Shen, 2002). Since hand geometry is cost effective, comfortable, and already gets user acceptance, it can be one of the ideal choices for verification. One can use finger length, thickness, and curvature for the purposes of verification. Moreover, hand geometry data is easier to extract and collect. Furthermore, hand geometry can be easily combined with other biometrics, namely fingerprints, where fingerprints can be used for (infrequent) identification and hand geometry for (frequent) verification. In case of other biometric system, the reliability of personal identification using face is low, as the researchers today continue to grapple with the problems of pose, lighting, orientation and gesture (Kumar, et al, 2003). Good frictional skin is required by fingerprint imaging system. Among other identification techniques, a special illumination setup is needed for iris or retina based identification system (Jain, et al, 1999). The existing hand geometry based personal verification systems discussed in (Kumar, et al, 2003), (Jain, et al, 1999), and (Sanchez-Reillo, 2000)-(Kong and Zhang, 2002) have several critical problems. They need extensive features to extract, such as lengths, height, widths of different fingers and so on. Therefore, even a slight displacement of hand can make it difficult for them to acquire data non-intrusively. Moreover, a minor pressure on hand may change the lengths, heights and widths of different fingers and palm and provide wrong data. To acquire correct data, some researchers, like (Jain, et al, 1999), take help from special components (like pegs), which is uncomfortable for the user. All the methods discussed above need complex image acquisition systems and their efficiency is not very high. The total success rate (TSR) of the technique given in (Sanchez-Reillo, 2000) is 97%, whereas TSR of (Jain, et al, 1999) is only 94.99% and the TSR of (Kumar, et al, 2003) and (Oden, et al, 2001) is respectively 91.66% and 89%. Hence there are scopes for further research in this area to attain higher accuracy.

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3. Multribiometric Systems Multibiometric systems represent the fusion of two or more unimodal biometric systems. Such systems are expected to be more reliable due to the presence of multiple independent pieces of evidence. Several factors should be considered when designing a multibiometric system. These factors include the choice and number of biometric behaviors; the level in the biometric system at which information provided by multiple types should be integrated; the methodology adopted to integrate the information; the cost versus matching performance trade-off; and system is user friendly or not. It must be noted that deploying a multibiometric system introduces some overhead in terms of computational demands and costs. Therefore, it is important that the cost versus performance trade-off is carefully studied before deploying these systems. In this paper we consider the combination of two unimodal systems which are hand geometry and finger stripes geometry. The features of both unimodal systems can be captured by taking one hand image. Therefore, image acquisition process is very much user friendly and do not increase system overhead. Moreover, some of the limitations imposed by unimodal biometric systems can be overcome by using multiple biometric modalities (Andrijchuk, et al 2005, Uludag et al 2004).

4. Research Methodology The whole process of the proposed verification system can be divided in to five steps. They are image acquisition, image pre-processing, feature extraction, training and verification using BP of ANN. These steps are described in the following sections. 4.1. Image Acquisition A color image of a hand is acquiesced by an image capturing device, setting the mode 300 dot/inch due to make the process faster. A prototype for image acquiescing is shown in Figure 1. Acquiesced image is then reduced to 25% both horizontally and vertically to make the image size small. The fingers should lie adjacent to each other and must not be overlapped. The user can place a hand freely since there is no peg to fix the position of the hand. Figure 1: An image is acquiring by the prototype

264 4.2. Image Pre-Processing

Farhat Anwar, Md. Arafatur Rahman and Md. Saiful Azad

Image dimension may vary due to the irregularities in the image scanning and capturing process. For this reason, a normalized image size of 360×270 pixels is considered. Before the normalization, the aspect ratio between width and height of an image is not exactly the same what we require. Therefore, equations (1) and (2) are employed to normalize the image. (1) Xi = (X´i / Xmax) * Mx Yi = (Y´i / Ymax) * My (2) where Xi, Yi are pixel coordinates for the normalized image, and X´i, Y´i are pixel coordinates for the original image; M is the dimension (width or height) for the normalized image. These equations are applied in Figure 2 (a) and the result is shown in Figure 2 (b). A color image consists of a coordinate matrix and three color channels. Coordinate matrix contains, x, y coordinate values of the image. The color channels are labeled as Red, Green and Blue. Since our approach demands finger stripe images in gray scale, we have to convert the color image into gray scale. The color image is converted to gray scale using the equation (3) as in Figure 2(c). Gray Color = (Red + Green + Blue) / 3 (3) Subsequently, thick boundary (edge) outside the fingers is generated using edge detection algorithm (Rafael Gonzalez and Richard, 2001) given in equation (4). The result is depicted in Figure 2(d). (4) Xn = (Xn+2 + 2X n+3 + X n+4) – (X n-4 + 2X n-3 + X n-2) where, Xn is the new pixel value for the detected edge. To detect the top points of the fingers (T1, T2, T3 and T4) searching starts from the top-left to the right and move downward according to the pixel value. Searching will result in detecting the toppoint of the middle finger first. Subsequently, a new searching is used from the top-point of the middle finger towards the right side to detect the top-point of the index finger. Then another searching starts from the top-point of the middle finger towards left side to identify the top-point of the ring and little fingers as in Figure 3(e). Along with top points, it is necessary to find the valley points (V1, V2 and V3) of different fingers. Lines are drawn according to the colour intensity from those points to construct tree (Tr1, Tr2 and Tr3) which will place different fingers into different regions as shown in Figure 3(e). 4.3. Feature Extraction Since in this paper multi-biometric technique is considered, it is necessary to extract features for each of them separately i.e. two sets of feature vectors is needed. Figure 2: Steps of the feature extraction.

(a) Image

(h) Finger stripes

(b) Normalized Image

(c) Gray Image

(d) Detecting Edge

(g) Hand Geometry

(f) Point Selection

(e) Region Selection

Multibiometric Systems Based Verification Technique 265 4.3.1. Finger Stripe Geometry In finger stripe geometry, we have 24 feature vectors; half of them are width of finger stripes and the rest are the mid-point distances of the stripes which are shown in figure 3(h). For extracting this features following steps need to be considered. Step 1: Calculating Finger Height: Finger height is calculated using the following equations, Finger Height for Index = Tr1+ T1(y) – V1(y) (5) Finger Height for Middle = Tr1+ T2(y) – V1(y) (6) (7) Finger Height for Ring = Tr2+ T2(y) – V2(y) Finger Height for Little = Tr3+ T3(y) – V3(y) (8) Where Ti (y) is the y axis value of top point and Vi (y) is the y axis value of valley point. Step 2: Calculating Reference Width of the Stripes: Using the following equations, reference width of the stripes can be calculated. They are, (9) Reference width of Index = (V1(x) - T1(x)) * 2 Reference width of Middle = (V2(x) – T2(x)) * 2 (10) Reference width of Ring = (V3(x) – T2(x)) * 2 (11) Reference width of Little = (V3(x) – T3(x)) * 2 (12) Where Ti(x) is the x axis value of top point and Vi(x) is the x axis value of valley point. Step 3: Finding the Stripes: For finding the stripes, first we’ll divide each finger into three equal regions. Then we need to find out such a stripe in each region whose width will be closer to the reference width of that finger. Step 4: Mid-points of the Stripes: To calculate mid-point of a stripe, a line is drawn from the top point of the finger which is parallel to the tree. This new line intersects the stripes. We consider these intersect points as the mid-points of the stripes. The distance of these mid-points are calculated. The width of the finger stripes and the mid-point distance of stripes which are calculated previously in step 3 and step 4, is our feature vectors for finger stripe geometry. 4.3.2. Hand Geometry with Complete Graph Technique From the processed image, top points of the four fingers (except thumb) and the mid point of the first stripe of the little and index finger are detected. These six points are considered as the vertex of a complete graph (West, 2001). Therefore, the number of edges of this complete graph is 15, which can be calculated using equation (13). These fifteen edges are considered as the feature vectors of hand geometry. Number of Edges = n × (n-1)/2 where n represents the number of vertexes. (13)

5. Training Using ANN Three different images of same person are collected for ANN training purpose. Back propagation algorithm (Haykin, 199) is selected among various other learning algorithms of ANN for its linearity and powerful mapping of network. Feature values are extracted and stored in a database after assigning a unique integer ID. One of the important aspects of back-propagation is to design proper network architecture. Success of the algorithm depends on the size of the neural network, since it is difficult to train a very large network. As the amount of the training data increases, difficulty also increases. For this reason, network architecture is kept as simple as possible in this proposed technique. Our artificial neural network shown in Figure 3, comprises of the followings • Multilayer fully connected feed forward network • 15 input nodes and 1 output node • 4 hidden nodes • Slope parameter, α = 4

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• Learning Rate, η =0.01 For getting faster performance, multilayer fully connected feed forward network is considered. Since, based on the 15 features the out put will be generated either valid or invalid user therefore 15 input and 1 output nodes are preferred. In the experimental analysis, it has been observed that the network with 4 hidden nodes along with α = 4 and η=0.01 performs smoothly. Before verification, we need to train our neural network. Training phase begins only when the user doesn’t provide any ID. The system treats him as a newcomer. Three hand images are collected from the new user. Feature values are extracted from the hand images. Extracted feature values are used to train a Neural Network until error falls below the threshold. Back-propagation algorithm is used in such a way that, for a match pattern, the output will be generated 1 from the single output node. Otherwise, output will be varied from 1. After the training is over, error and weight vectors of the network will be stored with a newly assigned unique personal ID. The person is informed about this ID which is used later by him for his verification. Figure 3: Artificial Neural Network with 15 inputs, 4 hidden nodes and 1 output In p u t N o d e s

H id d e n N o d e s

O u tp u t

1 1 2 2 3

1 3

4 15

6. Verification Using BP Verification phase begins when the user provides his ID and his hand image. Feature values are extracted from his hand image. These extracted feature values of newly collected hand’s image will be the input of a neural network for forward propagation. This network is retrieved from the database using the ID provided by the user. If the Error after forward propagation is within the range, then it will consider the person as a valid user, otherwise an invalid user. The error is calculated as, Error = Ideal output – Actual output = 1 – Actual output Where, Ideal output = 1 and Actual output is the value, we get from the forward propagation. The flow chart of the proposed system is demonstrated in Figure 4. In the first step of the verification process, the user will provide his ID to the software and hand image to the scanning device. If stored ID is matched with given ID, only then hand image is considered for verification process. Collected image is then normalized, which is called image acquisition. In the second step, feature vectors are extracted using feature extraction algorithm from normalized image. On these feature vectors both hand geometry and finger stripes geometry are applied. The extracted results are compared with the result in the database. For verified, one need to pass acceptable label (minimum threshold value) of the both processes and pass maximum level (maximum threshold value) of at least one process. Those who failed to pass any of the above describe criteria, are rejected. For new user, the feature vectors of this hand image are collected and stored in the database.

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Figure 4: Flowchart of the proposed method Im a g e A c q u is i t i o n

F e a t u re E x t ra c t io n

V e ri f i c a t i o n P r o c e s s U s in g F in g e r S t ri p e s G e o m e t ry (F G S )

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V e rif ic a t io n P ro c e s s U s i n g H a n d G e o m e t ry (H G )

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I f b o t h a re Y t h e n o u t p u t w i ll b e Y o t h e rw is e N

I f b o t h a re Y t h e n o u t p u t w il l b e Y o t h e rw is e N

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I f b o t h a re Y t h e n o u t p u t w il l b e Y o t h e rw i s e N

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If o n e o f th e i n p u t i s Y th e n o u tp u t w i l l b e Y o th e r w i s e N

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V e ri f ic a t io n C o m p l e t e d (Y in d i c a t e s v a li d u s e r a n d N in d i c a t e s in v a li d u s e r )

7. Experimental Evaluation The dataset is collected from 200 different persons, each person having 3 images. The feature vectors are extracted as mentioned earlier in section 4.3 and the training and verification processes are also described in section 5 and 6 respectively. To evaluate the system performance, three well-known measurements are used, False Rejection Rate (FRR), False Acceptance Rate (FAR), and Total Success Rate (TSR). FAR of our proposed system is 1.33%, whereas FRR is 0.5% and TSR is 99.50%. A comparison among the results of the proposed method and the results of the existing methods is illustrated in Table 1. From the table, it can be concluded that the proposed system performs better than existing systems.

268 Table 1:

Farhat Anwar, Md. Arafatur Rahman and Md. Saiful Azad Comparison between the proposed method and other existing hand geometry based methods

Name of the Paper Biometric Identification through Hand Geometry Measurement (Niennattrakul and Ratanamahatana, 2007) A prototype Hand Geometry Based Verification System (Jain, et al,1999) Personal Verification using Palmprint and Hand Geometry Biometric (Kumar et al, 2003) Hand Reorganization using Implicit Polynomial and Geometric Features (Oden, et al, 2001) Finger Stripe based Verification System (Rahman, et al, 2007) Complete Graph based Verification System(Rahmanm et al, 2007) Proposed Technique

Techniques Applied Feature Vector Classification for Verification Dimension Success Rate (%) Euclidian distance 15 86 metric Gaussain Mixture 21 97 Models (GMMs) Absolute Distance 14 94.99 Metric Normalized 16 91.66 Correlation Geometry 16 89 ANNs DBNN DBNN BP

24 24

95.07 98.79

15

99.11

24/15

99.50

8. Conclusion In this paper, a new multi-biometric based verification system is proposed using hand geometry and finger stripe geometry. It has been demonstrated that this method is efficient due to its high success rate. It is simple since by taking one image from the user we can get all the features for both the unimodal biometric employed. Users can place their hands freely without requiring pegs to fix the placement of their hand. Thus it would be convenient for practical implementation. In future, an attempt will be made to develop a person identification system by using multibiometric technique.

Acknowledgement The authors would like to express sincere gratitude to Malaysian Government for providing fund for the research under Fundamental Research Grant Scheme (FRGS) IIUM/504/RES/G/14/3/01/LT38.

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