Mixing Fingerprint Features for Template Security Shancymol Sojan 1,R.K.Kulkarni 2 1 PG scholar, 2 PhDProfessor,

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1 Mixing Fingerprint Features for Template Security Shancymol Sojan 1,R.K.Kulkarni 2 1 PG scholar, 2 PhDProfessor, Electronics and Telecommunication Dept, VESIT, Mumbai, Maharashtra, India Abstract Most of the authentication systems today don t just use traditional systems like password or PIN but also rely on biometrics for a better security. Securing the biometric template is thus becoming a major concern in biometrics based systems. Although it is possible to use different biometric traits in combination (multi- modal biometrics) to enhance security, it is computationally expensive and time consuming. This paper explores the possibility of combining features from the same biometric trait (fingerprints) i.e keeping it unimodal but still offering the same security as that of the combined biometrics. The algorithm consists of extracting minutiae and orientation features from two different fingerprints and producing a mixed template using these extracted features. The template produced is tested using correlation based matching technique and the correlation co-efficient is calculated. The advantage of creating the mixed template is that it is cancellable and serves as a new virtual identity. Keywords minutiae,orientation, normalizaiton,spurious minutiae, cross numbering, mixing template,tempalte security,correlation-based matching. I. INTRODUCTION Authentication and security are one of the major concerns of today. Authentication based on passwords and PINS have a risk of being stolen or misused. In such a case, biometrics acts as an effective solution as it offers high security over these methods. Biometric traits can be either physiological like fingerprint, iris, face, palm or behavioral like gesture, gait etc[1],[2].out of these, fingerprints are the most commonly used biometric trait. Recently efforts are directed towards securing the stored templates as template theft is a serious issue wherein it is difficult to revoke or reissue a stolen template. The existing template protection techniques can be classified into biometric cryptosystem (key-binding and key-generation), feature transformation method (salting and non-invertible transforms), multimodal techniques (for eg.face + iris), feature combination methods, etc.biometric cryptosystems are also called helper data-based method. In the key binding scheme, a key is chosen and binded to the biometric template whereas in key generation scheme, keys are generated with the help of the given biometric and a helper key and in the matching process the keys are matched. Examples of bio-cryptosystems [3] are fuzzy commitment scheme, fuzzy vault technique [4],shielding functions [5], quantization schemes[6], etc.these method helps in hiding the user identity but has an inevitable drawback that it is very much possible to reconstruct the template if the key and the protected fingerprint is stolen thus compromising the user sidentity. Methods based on feature transformation are noninvertible transforms and salting approach. Teoh et al. proposed a bio hashing approach based on the inner products between the user s fingerprint features and a tokenized pseudo-random number[7].ratha et al. proposed to generate cancelable fingerprint templates by applying non-invertible transforms on the minutiae. This transform will usually lead to a reduction in matching accuracy as it is performed by a key [8].The methods based on bio hashing approach, non invertible transform approach provides better accuracy however it is vulnerable to intrusion and linkage attacks when both the key and the transformed template are stolen. The methods based on combining templates can be either combining features from the same biometric trait or using two or more biometric traits (mutimodal)[9]. ErenCamlikaya et al.[10] presented a multimodal biometric verification system by mixing fingerprint and voice modalities where minutiae from fingerprint is combined with artificially generated points from voice. Multibiometric template scheme has been used for providing robust integrity among fingerprint templates and difficult for the attacker to distinguish a mixed fingerprint from the original fingerprints, however the ridge enhancement is not addressed in these approaches. Yanikoglu et al.[11] presented two distinct fingerprints into a single identity either in the image level or in the feature level. In this the minutiae features of the two fingerprints are combined. The drawback here is that the combined template will have more minutiae points than the single fingerprint which makes it very easy for the attacker to guess. Arun Ross[12], [13], [14] presented the method in which the spiral and continuous components of two different fingerprint are mixed.in visual cryptography method [3], two sheets are generated from fingerprint and they are ISSN: Page 355

2 overlaid to produce a temporary template. However, there is a practical limitation in storing the sheets in separate databases owing to which it can t be used in many applications. In [15], Feng has combined minutiae features of two fingerprints. In [16], the combined fingerprint template is generated by combining minutiae and orientation points from different fingerprints. The advantage here is that it is possible to use the existing matching techniques on the combined template and it helps to create a new virtual identity. Papers [17-21] discuss fingerprint feature enhancement techniques. Minutiae feature or Galton feature extraction methods are discussed in [22-29]. It can broadly be classified into those that work on binarized images and those that work on grey scale images. Methods that work on binarized images are thinning-based & non-thinning based. Examples are chain code based, morphology based, run representation, cross numbering based, etc. Methods that directly work on gray-scale images are ridge line following based and fuzzy based. Among these, cross numbering based method which works on thinned images is most widely used. The orientation extraction methods are discussed in [30-35] which can be categorized as pixel-alignment based method, local method and global method. The accuracy of pixelalignment based method is limited to fixed number of discrete reference orientation values and computational complexity increases proportional to the number of reference orientations.some of the local methods are filter bank, wavelet projection, gradient based& spectral estimation of which the gradient based approach gives a more accurate result. Global methods are based on mathematical modeling of data. Though model based approach is robust to noise, the inefficiency in developing a perfect mathematical model to represent fingerprint of every individual is deterring it from being a popular approach. Fingerprint matching methods are discussed in [36-41]. The rest of the paper is organized as follows: Section II discusses the proposed mixed template recognition system. Section IIIdiscusses the matching methods and the parameters to evaluate the performance of the system. Section IV gives the results of minutiae and orientation extraction followed by conclusion and future work in section V. II. PROPOSED MIXED FINGERPRINT TEMPLATE RECOGNITION SYSTEM In the system, as shown in Fig.1, the minutiae and orientation features are extracted from fingerprints F1 and F2 respectively and using a coding strategy they are combined to produce a new template. This template is stored in the database and will be used for user verification. The advantage of creating mixed template is that it is more robust than single fingerprint matching. Even if a template is stolen, the identity of the user is not compromised. It is also possible to create cancellable templates using this technique. Thus it is a method that helps to create a whole new virtual identity to the user and also gives security to the biometric template. Fingerprint 1 Fingerprint 2 Fig 1.Proposed work A. Algorithm for minutiae generation Minutiae generation is done by the method of cross numbering as discussed in[29].in cross numbering method, 8-connected ridge flow pattern of skeletonized image is used and by scanning the local neighbourhood of each pixel, CN value is computed. CN =1 corresponds to a ridge ending, and a CN =3 corresponds to a bifurcation. The steps are as follows: (i) Preprocess the image using binarization and thinning process. (ii)use theminutiefunctionto find CN and categorize the points as ridge ending or ridge bifurcation. (iii) Remove spurious minutiae. Minutiae points CODING Orientation points (iv)save the minutiae points i.e. the coordinates and minutie direction. B. Algorithm for orientation generation The angle formed by the horizontal line with the ridge inclination is called as orientation[33]. Orientation computation is a block-wise operation and the steps are as follows: (i) Preprocess the image using normalization technique. (ii)divide the image into wxw overlapping blocks. Mixed Template ISSN: Page 356

3 (iii)compute gradients and using Sobel filter. (iv)compute average gradient vectors and. (v) Estimate the direction. Once the minutiae and orientation are extracted from the respective templates, unique mixed template is created. C. Algorithm for mixed template generation Let the first fingerprint be F1 and the second one be F2.The features extracted from both the fingerprints are combined to generate a single mixed template. (i)extract minutiae points from F1. (ii) Extract orientation points from F2. (iii)linear combination of F1 and F2. (iv)generate the binary ridge pattern of the combined image (v)generate the combined minutiae. (vi) Apply 32 point FFT to enhance the image. (vii)apply noise removal steps to reconstruct the template that looks similar to a real fingerprint. The mixed template thus formed is stored in the database. III. MATCHING TECHNIQUES AND PERFORMANCE PARAMETERS Fingerprint matching methods can be broadly classified as correlation based [40], minutiae based [36-38] and non-minutiae based matching [42].In minutiae based method, the minutiae points from the test and query fingerprints are computed and the matching score is computed. In correlation based method, the correlation coefficient is used to compute the degree of matching between two fingerprints. In non-minutiae based matching, parameters other than minutiae like core points, delta points etc can be computed and matched. A matching threshold can be set in order to distinguish the genuine and imposter fingerprints. The most common performance parameters of fingerprint based system are and where is the false rejectionrate and is the false acceptance rate. Using these parameters the Receiver Operating Curve ( ) can be plotted. The accuracy of the system can be computed using statistical parameters like precision ( ), recall ( ) and F-measure ( )..Precision of positive predictive value denotes the percentage of selected items that are correct. Recall or true positive rate or sensitivity is the percentage of correct items that are selected. F- measure of F-score is the indication of the accuracy of the test. It is calculated as the harmonic mean of and given by equation (i). The accuracy is given by equation (ii). (i)...(ii) where stands for true positive, stands for true negative, stands for false positive and stands for true negative. IV. RESULTS AND DISCUSSION The minutiae points are extracted as per the algorithm in section 2.1, the orientation points are extracted as per algorithm in section 2.2 and the mixed template is generated as per algorithm in section 2.3. The first fingerprint is taken from database FVC 2000 and the other is from database NIST. The mixed template generated is shown in Fig.2. Two such databases were created, one with the first impression of the input fingerprints called the test database and the other with the second impressions of the input fingerprints called the query database. The purpose of creating the query database is to test the system against attack on the template. Some of the mixed templates created are shown in Fig 3. Matching the templates was done using correlation based method. The correlation coefficient, efficiency and match time have been computed for each template. Two scenarios are considered: (I) when the exact two input fingerprints are used for verification ;(II) when a slightly different impression of the exact fingerprints are used or verification. The system was accurately able to identify the correct user in both the scenarios. (a) (b) (c) (f) (e) (d) ISSN: Page 357

4 Fig 2. Mixed template generation: (a) F1 output,(b) F2 output,(c) combination of F1 and F2,(d) combined minutiae (e)noisy result after enhancement,(e) mixed template. The template registration time has three aspects; the minutiae extraction time, orientation extraction time and the mixed template generation time. Even if this time is more compared to a single fingerprint feature extraction, once the templates have been registered, what matters is the verification time. The one-to-one matching time and other performance parameters for scenario II for a few templates are as seen in Table 1. Precision, recall, F-measure for the templates are as shown in Table 2. Input 1 Input2 Mixed template Table 2. Statistical parameters Template Precision Recall F-measure V. CONCLUSION AND FUTURE WORK In this paper, a template security method has been proposed. The minutiae and orientation features from two fingerprints are used to synthesize a new mixed template. The performance evaluation of the system shows that it is possible to use the same matching methods available for single fingerprint and achieve is acceptable accuracy and verification time. Here the matching is performed using correlation-based method but the correlation coefficients are lower due to the nature of the mixed template. Future work would be to use feature based matching of the templates and try to improve the performance of the system. VI. REFERENCES Fig 3. Results of mixed template generation Average template registration time is about seconds and the average verification time is about secs. Table 1. Performance Evaluation of the system Template Registration time (secs) Correlation coefficient Accuracy Verification time (secs) [1] Delac, Kresimir, and MislavGrgic. "A survey of biometric recognition methods." Electronics in Marine, Proceedings Elmar th International Symposium. IEEE, [2] Maltoni, Davide, et al. Handbook of fingerprint recognition.springer Science & Business Media, 2009 [3] Ross and A. Othman, Visual cryptography for biometric privacy, IEEE Trans. Inf. Forensics Security, vol. 6, no. 1, pp ,Mar [4] K. Nandakumar, A. K. Jain, and S. Pankanti, Fingerprintbased fuzzy vault: Implementation and performance, IEEE Trans. Inf. Forensics Security, vol. 2, no. 4, pp , Dec [5] Huixian, Li, et al. "Key binding based on biometric shielding functions."information Assurance and Security, IAS'09. Fifth International Conference on. Vol. 1. IEEE, [6] Chua, ShingChyi, EngKiong Wong, and Alan Wee Chiat Tan. "Fingerprint Singular Point Detection via Quantization and Fingerprint Classification."World of Computer Science & Information Technology Journal 5.12 (2015). [7] B. J. A. Teoh, C. L. D. Ngo, and A. Goh, Biohashing: Two factor authentication featuring fingerprint data and tokenised random number, Pattern Recognit., vol. 37, no. 11, pp , 2004 [8] N. K. Ratha, S. Chikkerur, J. H. Connell, and R. M. Bolle, Generating cancellable fingerprint templates, IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 4, pp , Apr [9] B.Yanikoglu and A. Kholmatov, Combining multiple ISSN: Page 358

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