Biometrics Verification Techniques Combing with Digital Signature for Multimodal Biometrics Payment System

Transcription

1 2010 International Conference on Management of e-commerce and e-government Biometrics Verification Techniques Combing with Digital Signature for Multimodal Biometrics Payment System JuCheng Yang School of Information and Technology Jiangxi University of Finance and Economics, Nanchang, , China Abstract The multimodal biometrics payment is a new technology that allows people to pay with their own biometrics such as fingerprints, face, hands and so on. In this paper, the biometrics verification techniques combining with digital signature for multimodal biometrics payment system are introduced. Considering the high universality, distinctiveness, easy collectability of fingerprint and face, a multimodal biometrics verification system with fingerprint and face as inputs is designed, and the hybrid fingerprint features and infrared (IR) face features for matching is to overcome the shortcomings of the traditional methods and grantee the integrity of the registered multimodal biometrics data. And then nine authentication models for authenticating an open network to ensure the integrity of these data are analyzed respectively. At last, a digital signature procedure with the Public Key Infrastructure (PKI) to illustrate a multimodal biometrics payment system with safe model is proposed. The proposed system can be applied to some public key platforms, too. Keywords-multimodal biometics payment system; authentication models; digital signature; third trusted party (3)Secure: their biometrics are unique to them, so only the owner can access their financial accounts, and no one can see their account numbers at check-out, or how they choose to pay. The authentication of the enrolled data is the key part of the payment system. Biometrics has the characteristics such as difficulty to be lost or forgotten, extremely difficulty to copy, share, and distribution. Fingerprint is one of the biometrics, which has high university, distinctiveness, easy collectability, and high performance. So it is able to be used for the payment system which needs high security to identify the person are genuine owner. For the data translation securely in public network, digital signature is a popular way to ensure the integrity of the data [2]. One of the biometrics payment approach is the fingerprint payment system as below Figure 1, people just place their finger on the fingerprint reader, then enter their own personal identify number (PIN) and choose the payment method. Then people had done the payment with their fingers. The processes are without signing and swiping. I. INTRODUCTION A payment system is a system for the transfer of money, which employs cash-substitutes. Traditional payment systems are negotiable instruments such as drafts (e.g., checks), credit cards and other charge cards, documentary credit and electronic funds transfers. But these payment systems suffer the shortcomings that the tokens or passwords to ensure the security of the systems are easily lost, forgotten, copied, shared or distributed. Biometrics payment is a kind of technology that allows people to pay at shops or markets with just touch of their fingers, moving their face or laying up their hands. Different with the normal payment methods, they won t need tokens or passwords except their own biometrics. When people use a biometrics payment system, their account information in the bank is automatically recognized to finish the payment procedure. Biometrics payment has the following advantages [1]: (1)Fast: no writing checks and no swiping cards. (2)Easy: no fumbling with cash. People can leave their wallet behind. (a) (b) (c) (d) Figure 1. Paying with fingerprint (a)place finger on the fingerprint reader. (b)unique points from finger identify person. (c) Enter PIN and choose fingerprint payment method. (d) Payment with no signing, no swiping. However, due to the complex input conditions such as input with broken fingers, smeared fingers and fuzzy fingers, it is difficult for extracting all the correct minutiae. So the traditional minutiae-based fingerprint identification system may cause identification failure. In addition, based on a single biometric verification technology (fingerprint, face or hand recognition), there are demerits: some biological features missing (such as broken finger), injuries (such as damaged fingers), disease (such as cataract), or feature collection of poor quality (such as light changes in face /10 $ IEEE DOI /ICMeCG

2 recognition), these factors will result in non-robust, poor reliability, weak identification [3]. Comparing with single biometric verification technologies, multimodal ones have clearly advantages. Jain et al. [4] show that the multimodal biometric is able to integrate various single biometric verification, and uses of the merits of all kinds of single biometrics to improve the performance of the system and achieve a more robust system with noise immunity, universality, reliability, security. Multimodal biometric is widespread in recent years and becomes a research focus. However, the choice of biometrics is crucial, according to Ross et. al. [3], both fingerprint and face verification methods are widely used and have their own merits. Face verification is the best supplement to the fingerprint verification system. On the other hand, the complex system is proportional to the number of input biometrics. For a payment system, less time consuming is need. The fingerprint and face biometrics are proved to ensure the security of identification with high speed [3]. So, in this paper, we explore a multimodal biometrics authentication technique including fingerprint and face biometrics. Usually, there are two well-known fingerprint verification methods: (a) minutiae-based methods (b) image-based methods. The minutiae-based methods [5-6] use several characteristics of minutiae such as type, position, orientation, etc for matching. However, these methods may suffer from several shortcomings. Image-based methods [7-8] use features other than characteristics of minutiae from the fingerprint ridge pattern, such as local orientation and frequency, ridge shape, and texture information. The features for these methods may be extracted more reliably than those of minutiae. To combine both merits of the two methods, in this paper, we propose a hybrid approach using both global minutiae and invariant moments for fingerprint verification [9-10]. These methods have higher performance comparing with other famous image-based methods, so we choose to use these technologies to ensure the integrity of the acquired data. Face recognition, being passive and non-intrusive, has a natural place in biometrics. Over the last decades, researchers from multiple disciplines have endeavored to build machines capable of automatic face recognition with visual [11]. However, it has been shown that even the well-known face recognition systems perform rather poor in less controlled situations. As a face acquired in visual has significantly different appearance due to the large variations both in intrinsic (pose, expression, hairstyle, etc.) and extrinsic conditions (illumination, imaging system, etc.), it is difficult to find the unique characteristics for each face, and it is accordingly not easy to develop a reliable system for face recognition by using visual. While visual represent the reflectance information of the face surface, IR face contain more fundamental information about faces themselves, such as anatomical information; the thermal characteristics of faces with variations in facial expression and make-up remain nearly invariant, and the tasks of face detection, location, and segmentation are relatively easier and more reliable than those in visual. In this paper, we adopt to use a method which uses the blood perfusion data for face recognition from the skin heat transfer (SHT) model, based on one thermogram. In the following section, the SHT model is derived based on thermal physiology. The aim is to convert the thermograms into blood-perfusion data to alleviate the internal and external variations especially resulting from ambient variations. In summary, we propose to use hybrid fingerprint features and IR face features for matching. These features were sophisticated for matching and they were proved of being able to overcome the demerits of the traditional methods with high accuracy performances [10][12]. Then, we analyze some authentication models to implement a multimodal biometrics payment system. At last, a procedure with digital signature to illustrate the payment system with safe model is proposed, too. The paper is organized as follows: multimodal biometrics verification system is briefly introduced in section 2. In section 3 the authentication models are explained in details. And nine safe models for multimodal biometrics payment system are illustrated in section 4. Finally, conclusion remarks are given in section 5. II. MULTIMODAL BIOMETRICS VERIFICATION SYSTEM The core composition of the multimodal biometrics payment system is the multimodal biometrics verification system. It contains two stages: the enrollment stage and the matching stage, as shown in Figure 2. The enrollment stage: the multimodal biometrics of the different individuals to be verified are first processed by feature extraction modules; the extracted features are stored as templates in a database for later use. The matching stage: the multimodal biometrics image of the individual to be verified first processed by feature extraction modules; the extracted features are then fed to a matching module with his/her identity ID, which matches them against his/her own templates in the database. The enrollment stage is realized in the registration step, however, the matching stage works when the multimodal biometrics payment is adopted

3 Biometrics Biometrics Biometrics Biometrics Enrollment Matching Fusion Database Authenti cation Fusion PIN Accept/ Reject Figure 2. Overview of our multimodal biometrics verification system. A. Fingerprint feature extraction The algorithm of a hybrid approach using both global minutiae and invariant moments for fingerprint verification is summarized as below: Step 1. Preprocessing with enhancement. The first step is to preprocess the fingerprint image, which includes fingerprint image segmentation, enhancement, binarization and thinning. Step 2. Minutiae and reference point determination. In the second step, we determine the minutiae and reference point respectively. The minutiae can be extracted from the thinning image by using the Crossing Number (CN).The core point is defined as the point of maximum curvature of the concave ridges, and is determined by using the complex filtering method [9]. Step 3. Fingerprint alignment. We use the reference point to align the input fingerprint image with the template image. The rotation metrics is used to align both in the same orientation, and the translation vector is used to align the relative distance of minutiae in both. Step 4. extraction. We present the global matching algorithm based on the aligned fingerprints. Firstly, the global minutiae feature vectors are extracted. We also compute the seven invariant moments [9] (n=1, 2 7) of the rectangle region of interest (ROI), which centered on each minutia and the center of the rectangle s direction is the same with the orientation of the minutia. Then the fingerprint feature vector consists of minutiae feature and for seven invariant moments for each minutia. B. Face feature extraction The algorithm of using the blood perfusion data for face recognition from the skin heat transfer (SHT) model is summarized with the following steps: Step 1. Image preprocessing. The first step is to preprocess the face image. First, we acquire the IR image through the infrared camera. When considering noise reduction and keeping effective information, our experiments have demonstrated that rank order filters and morphological filters are the most suitable filters to those impulse noises. Step 2. Face detection. We notice that the outer boundaries of faces, i.e., external boundaries (or we call black boundaries) exist in IR is thermally separated from its surrounding environment. And such information is independent of imaging conditions, so the face can be localized by detecting the face contour. This results in a binary segmentation map with value 0 indicating background and 1 indicating face. Step 3. SHT model. In order to extract the stable and consistent physiological facial features, Wu et. al.[12] have proposed a blood perfusion model which convert the temperature information of the face image into body s biological information: 4 4 3M 1 2 M M 1 ( T Te) Akd f ( Pg r ) ( T Te) ks( Tc T) D b b ct b( a T) Where T and T e represent the skin temperature and ambient temperature respectively. See Wu et.al.[12] for more details. Step 4. extraction. After applying the SHT model, the facial image is converted into blood perfusion image, and then Eigenface features are extracted from the blood perfusion image by using the Karhunen-Loeve Transform (KLT). C. Fusion and Authentication Fusion is to combine these two kinds of biometrics and form the best output for the multimodal biometrics payment system. Usually, there are three fusion approaches for a biometrics system: fusion at feature extraction level, fusion at score level and fusion at the decision level. The latter two methods will lost more information than the first one. So here we adopt the approach of fusion at the feature extraction level. The algorithm can be summarized as below: Step 1: The data obtained from each biometrics sensor is used to compute a feature vector. Step 2: As the features extracted from one biometric trait are independent of those extracted from the other, it is reasonable to concatenate the two vectors into a single new vector. Step 3: The new feature vector now has a higher dimensionality and represents a person s identity in a different (and hopefully more discriminating) hyperspace

4 Step 4: reduction techniques (such as principle component analysis) may be employed to extract useful features from the larger set of features. In the authentication stage, the matching algorithm to match the extracted features of the template and input biometrics is through a matching sore. This matching score definition assumes that if two feature vectors belong to the same biometrics, they will be similarity that yield a larger matching score, whereas if they are different, their score will be still small. The matching can be done in different authentication models. (b) III. AUTHENTICATION MODELS The authentication models for authenticating are to authenticate the customer is the right person who has registration in the service of the multimodal biometrics payment system. Most multimodal biometrics based non-face-to-face network authentication systems are composed of at least two parties: the client terminal and the server side, which is used to collect multimodal biometrics data and to provide service respectively. The framework of multimodal biometrics application system is shown in Figure 3. Figure 3. Framework of multimodal biometrics application system. The study of the multimodal biometrics identity authentication application is mainly based on the models according to the multimodal biometrics template storage place and the verification place. The multimodal biometrics template storage place and the verification place may be held on the client side, the server side, or the third trusted party (TTP). The TTP may be an authorized institution or a smart card that can perform complex calculation, etc. Figure 4 shows three typical models. (a) (c) Figure 4. Three typical models for multimodal biometrics identity authentication application (a) Template stores and matches in client side (b) Both template stores and matches in the server side (c) Both template stores and matches in the TTP. Nine models based on the three typical models for multimodal biometrics identity authentication application will be introduced and their security vulnerabilities are analyzed. (1) Template stores in the client side and verifying in the client side: For this model, the server side does not execute the multimodal biometrics processing load, so the server side has sufficient processing resources. This model can be used when the server side trusts the client side processing. This system may encounter the threats such as the forgery of template. (2) Template stores in the client side and verifying in the server side: For this model, it is possible for a user to utilize only a trusted verifier (similar to ID certification) to judge and send sensitive multimodal biometrics data and template to the server. This system may encounter the threats such as the forgery of template, and the crack by sending the template. That is not a safe way to do it. (3) Template stores in the client and verifying in the TTP: In this model, it is assumed that a client does not trust a server but trust a TTP. Accordingly, a client (as well as a server) requests a TTP to perform verification and to process client capture-data. This system also encounters the same threats as the above models. Besides, the security of the TTP must be guarded. (4) Template stores in the server side and verifying in the client side: In this model, for the local client terminal, a temporaryuse terminal (e.g., credit authorization terminal) is taken into consideration. This model requires the registration of template to the server and the server trusts the client-side

5 processing. However, the illegal user may crack the client s matching program or verification result to crack the whole system. (5) Template stores in the server side and verifying in the server side: For this model, it is necessary that the user registers in advance with a server that has a trusted template. This model requires that the server trusts the data captured from a client. This system may be secure since it can ensure the correctness of the template and avoid sending the template data. (6) Template stores in the server side and verifying in the TTP: In this model, it is assumed that a client cannot trust a server, but trust a TTP and TTP can guarantee sufficient resources (processing power, memory etc.). Accordingly, a client (as well as a server) requests a trusted TTP to perform verification. This system may face the threat of cracking by sending the template. (7) Template stores in the TTP and verifying in the client side: For this model, it is assumed that a user wants to use multiple services on a temporary-use terminal, (e.g., common credit authorization terminal). A user registers in advance with a trusted TTP. A server is required to trust processing on the client side. This scheme faces the similar threat as Model (4). (8) Template stores in the TTP and verifying in the server side: For this model, it is assumed that TTP can not guarantee sufficient resources (processing power, memory etc.) and a user registers in advance with a trusted TTP. A server is required to trust the client capture-data and the template provide by the TTP. Taking account of privacy protection, a TTP is required to receive permission in advance to present user data and verifier data. This system may face the threat of cracking by sending the template. (9) Template stores in the TTP and verifying in the TTP: For this model, it is assumed that the server-side has no verification ability. A user registers in advance with a trusted TTP and does verification processing to a TTP. This model also requires the TTP (server) to trust client capturedata. This scheme faces the threat of cracking verification result. correctness of the template cannot be judged, and maintenance costs are high; the advantage of template storage in the TTP is that all terminals are available and maintenance costs are low, while the correctness of the template cannot be judged. Due to the multimodal biometrics payment system requiring high security, it is better to ensure that the template storage place and the verification place is the same. So model (1), (5), (9) can be considered. However, model (1) should be excluded because the correctness of template is not ensured. For model (9), the same problem will encounter, besides, the TTP (such as smartcards) will add the additional cost to the system, so this model should also be excluded. Model (5), on the contrary, has not such shortcomings of model (1) and (9) and the server (such as bank) has the ability to deal with millions and billions clients safely and it is possible to satisfy the numerous accesses at the same time. So it is a safe model for the multimodal biometrics payment system. The following just design a scenario using mode (5) to implement authentication procedure combing multimodal biometrics with digital signature as Figure 5. The processing procedure is summarized as below: Step 1: Client terminal sends service request to server; Step 2: Client terminal and server negotiate the authentication mechanism and models; Step 3: The user inputs its Public Key Certification (PKC) with the multimodal biometrics data; Step 4: The system digitally signs the user s multimodal biometrics data and sends to the AP server; Step 5: The system checks the multimodal biometrics data s integrity, if it is altered, the results will be noticed to the server; Step 6: The system checks the PKC, if it is altered, the results will be noticed to the server; Step 7: The system performs the multimodal biometrics matching, if matched, server will provides service, otherwise, the results will be noticed to the client with failure. IV. SAFE MODELS FOR MULTIMODAL BIOMETRICS PAYMENT SYSTEM According to template stores in different place, there are advantages and disadvantages of them: the advantage for template storage in the server side is that the correctness of the template can be judged, however, authentication may be not possible when the server is down to build a DB heavily; the advantage of template storage in the client side is that it can authenticate without using a network, but the Figure 5. Multimodal biometrics authentication procedure combing with digital signature

6 V. CONCLUSIONS In this paper, biometrics verification techniques for a multimodal biometrics payment system are introduced. A multimodal biometrics verification system with hybrid fingerprint features and IR face features is firstly proposed to grantee the integrity of the registered multimodal biometrics data, and nine authentication models for authenticating an open network to ensure the integrity of these data are explained respectively. At last, a digital signature procedure with the public Key Infrastructure (PKI) to realize a multimodal biometrics payment system with safe model is proposed. Further works need to improve the robustness and accuracy of the system. ACKNOWLEDGMENT This work is supported by the Science & Technology Project of Education Bureau of Jiangxi Province, China (GJJ10432), Jiangxi Nature Science Foundation (2009GQS0038), and it is also supported by the National Natural Science Foundation of China (No and No ). REFERENCES [1] [2] Y. Isobe, Y. Seto, M. Kataoka, Development of Personal Authentication System using Fingerprint with Digital Signature Technologies, 34th Annual Hawaii International Conference on System Sciences (HICSS-34)-Vol. 9, [3] A. A. Ross, K. Nandakumar, and A. K. Jain, Handbook of Multibiometrics, Springer-Verlag New York, Inc., Secaucus, NJ, USA, [4] A.K. Jain, K. Nandakumar, A. Ross, Score Normalization in Multimodal Biometric Systems, Pattern Recognition, [5] N. Ratha, K. Karu, C. shaoyun, and A. Jain, A real-time matching system for large fingerprint databases. IEEE Trans. On Pattern Anal. And Machine Intell. 18(8), pp , Aug [6] A.K. Jain, R.Bolle, and S. Pankanti, On-line fingerprint verification. IEEE Trans. On Pattern Anal. And Machine Intell. 19(4), pp , Apr [7] A.K. Jain, S. Prabhakar, L. Hong, S. Pankanti, Filterbank-based fingerprint matching, IEEE Transactions on Image Processing, Vol.9, pp ,2000. [8] J. C. Yang, J.W. Shin, D. S. Park, Fingerprint Matching Using Invariant Moment s, Lecture Notes in Artificial Intelligence(LNAI 4456), Springer, Berlin, [9] J. C. Yang, D.S. Park, A Fingerprint Verification Algorithm using Tessellated Invariant Moment s, Neurocomputing, vol. 71(10-12), pp , [10] J. C. Yang, J.W. Shin, B.J. Min, J.W. Lee, D.S. Park, S. Yoon, Fingerprint Matching using Global Minutiae and Invariant Moments, The 2008 International Congress on Image and Signal Processing, hainan, China, May, [11] W. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips, Face recognition: a literature survey, ACM Comput. Surv.35(2003) [12] S. Q Wu, W.S. Lin, S. L. Xie, Skin heat transfer model of facial thermograms and its application in face recognition, Pattern Recognition, 41, pp ,