FINGERPRINT RECOGNITION FOR HIGH SECURITY SYSTEMS AUTHENTICATION

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1 International Journal of Electronics, Communication & Instrumentation Engineering Research and Development (IJECIERD) ISSN X Vol. 3, Issue 1, Mar 2013, TJPRC Pvt. Ltd. FINGERPRINT RECOGNITION FOR HIGH SECURITY SYSTEMS AUTHENTICATION KAWADE SONAM P 1 & V. S. UBALE 2 M.E. Student, Electronics Dept., AVCOE, Sangamner, Ahmednagar, Maharashtra, India Assistant Professor, Electronics Dept., AVCOE, Sangamner, Ahmednagar, Maharashtra, India ABSTRACT Fingerprint Recognition is the reliable, most important and useful biometric technique used for person identification and verification. It uses ridge patterns on fingers which uniquely identify people. Fingerprint Recognition is possibly the most sophisticated method of all biometric techniques for authentication in High Security Systems. Even features such as persons gait, face or signature may change with passage of time and may be fabricated or imitated. However a fingerprint is completely unique to an individual and stayed unchanged for lifetime. KEYWORDS: Fingerprint Recognition, Hybrid Fusion, Serial Fusion, Parallel Fusion, High Security Systems INTRODUCTION Fingerprint Recognition Process In Fingerprint recognition technology, a sensor captures the fingerprint image and then pre-processing of image takes place. After that, image is thinned and its minutiae are extracted. This is compared with the Fingerprint images already stored in the database. Also they are matched using some matching algorithm. If the captured Fingerprint image is matched with Fingerprint image in the database, then Fingerprint image is successfully identified. Figure 1: Fingerprint Recognition Process Among all biometric techniques such as Speech recognition, Signature recognition, Iris Recognition etc., Fingerprint Recognition is most secure and accurate, because we have ten fingerprints as opposed to one voice, one face or two eyes. Each and every one of our ten fingerprints is unique, different from one another and from those of every other person; even identical twins have unique fingerprints. MAIN BODY OF THE PAPER Fingerprint Recognition In Fingerprint Recognition, Ridge patterns on fingers uniquely identify people. There are three major features of Fingerprints: arch, loop, whorl. Each fingerprint has at least one of the major features and many small features.

2 156 Kawade Sonam P & V. S. Ubale Features of Fingerprints Arch Figure 2: Features of Fingerprints finger. The ridges enter from one side of the finger, rise in the center forming an arc, and then exit the other side of the Loop The ridges enter from one side of a finger, form a curve, and then exit on that same side. Whorl Ridges form circularly around a central point on the finger. Classification of Features of Fingerprints Large volumes of fingerprints are being collected in everyday applications. To reduce the search time and computational complexity, classification is necessary. It allows matching of fingerprints to only a subset of those in the database. Numerous algorithms have been developed in this direction. Sensors Used for Image Acquisition

3 Fingerprint Recognition for High Security Systems Authentication 157 Figure 3: Sensors Used for Image Acquisition Optical Sensor It is the oldest, most widely used technology in which finger is placed on a coated hard plastic plate. Sensor captures image and charged coupled device (CCD) converts the image of the fingerprint, with dark ridges and light valleys, into a digital signal. It having the advantage that it is fairly inexpensive but it is less accurate. Silicon Based Capacitive Sensor Most technology based on DC Capacitance, but some also use AC Capacitance. The silicon sensor acts as one plate of a capacitor, and the finger is the other. The capacitance between the sensing plate and the finger is converted into an 8-bit grayscale digital image. They provides good resolution for the image, hence better image quality, with less surface area, hence cost is low. But, with the reduction in sensor size, careful enrolment and verification problem arises. Ultrasound Sensor This is the most accurate of the fingerprint technologies. It uses transmitted ultrasound waves and measures the distance based on the impedance of the finger, the plate, and air. They are capable of penetrating dirt and residue on the sensing plate and the finger. The quality of the image depends to a great extent on the contact between the finger and the sensor plate which could also be quite hot. Thermal Sensor This sensor is based on Pyro-electric material, which measures the temperature differential between the sensor pixels that are in contact with the ridges and those under the valleys, that are not in contact. This material is able to convert a difference in temperature into a specific voltage. It functions as well in extreme temperature conditions, but image disappears quickly.

4 158 Kawade Sonam P & V. S. Ubale Fingerprint Pre-Processing a) Original b) Orientation c) Binarised d) Thinned e) Minutiae f) Minutia Graph Figure 4: Fingerprint Pre-Processing Fingerprint Recognition in High Security Systems In high security systems such as ATM, Access control of nuclear power stations, an extremely low false accept rate (FAR) and as low as possible false reject rate (FRR) are desired at the same time, this is called as Double Low problem. Even a fingerprint system with very low equal error rate (EER) cannot achieve such a Double Low goal. Also it is difficult to solve this problem only by improving the performance of individual fingerprint identification algorithm. Hence, a Hybrid Fusion Method of fingerprint identification is proposed to solve Double Low problem. A system with low EER might have a bad performance in high security systems for its FRR will rise acutely when its FAR becomes very low. Hybrid Fusion Method Figure 5: Framework of Hybrid Fusion Method

5 Fingerprint Recognition for High Security Systems Authentication 159 Hybrid Fusion Method consists of Serial and Parallel Fusion Strategy. Serial Fusion Strategy Minutiae features are extracted after a query fingerprint image is acquired. Minutiae-Based Matching Algorithm It is used to match the query fingerprint with all fingerprints in the template database. In this, multiple matching scores are acquired. Then, Maximum matching score S1 is compared with a threshold T1. Identification is successful if the maximum score S1 is higher than T1, i.e. S1>=T1. Figure 6: Framework of Minutiae-Based Matching Algorithm Otherwise, means if S1<T1, then Ridge-Based Matching Algorithm In the same way, identification is successful if the maximum matching score S2 is higher than another threshold T2, i.e. S2>=T2. Query Fingerprint Template Fingerprint Pre-processing Pre-processing Clear_up Clear_up Dispersing Substructure Substructure Dispersing Substructure Matching Initial Substructure Pair Initial Ridge Pair Ridge Matching Match Score Otherwise, means if S2<T2, then Figure 7: Framework of Ridge-Based Matching Algorithm

6 160 Kawade Sonam P & V. S. Ubale PARALLEL FUSION STRATEGY Rank-Level Fusion Results of identification are obtained according to the Rule of rank-level fusion. Fingerprint identification system typically outputs a ranking or a candidate list instead of a match score or a Boolean value. So, the rank 1 template fingerprint is more similar with the query fingerprint than the rank 2 template fingerprint, and so on. The goal of rank-level fusion method is to combine ranks assigned by various fingerprint matchers to derive an integrative rank for each identity.there are three rules usually used to combine ranks assigned by different matchers: Highest Rank rule, Borda Count Rule, and Logistic Regression Rule. They are unsuitable for high security systems because the demand of Double Low is very strict; hence, Double Highest Rank Fusion Rule is used. Double Highest Rank Fusion Rule The query fingerprint is regarded to be successfully identified only when its identities corresponding with the highest rank of different matchers are same. Analysis Two fingerprint databases have selected, namely, FVC2002DB1 and FVC2002DB2. In each selected fingerprint database, there are one hundred fingers and eight hundred fingerprint images, and every finger corresponds to eight fingerprint images. One image of each finger to constitute the template database and the other seven images to constitute the query database for above two databases have selected. Five Methods are Used for Experiment Two individual methods: Minutiae-Based Matching Algorithm Ridge-Based Matching Algorithm Each Known as Individual Method 1 and 2 Respectively. Two fusion methods: Serial Fusion Method Parallel Fusion Method Each Known as Fusion Method 1 and 2 Respectively. Hybrid Fusion Method For the sake of comparing the performance of above five methods, a certain reference value of FAR must be fixed above all. Zero FAR is selected. Table 1: The Performance of Five Methods

7 Fingerprint Recognition for High Security Systems Authentication 161 Figure 8: The Performance of Five Methods As shown in Table 2.3, using the individual method 1 and 2, 19.1%, 51.8% fingerprints are falsely rejected at zero FAR on the average on FVC2002DB1 and FVC2002DB2, respectively. Using the fusion method 1 and 2, 14.8%, 9.4% fingerprints are falsely rejected at zero FAR respectively. Using the hybrid fusion method, only 6.6% fingerprints are falsely rejected at zero FAR. CONCLUSIONS Double Low problem and its characteristics are analyzed. Hybrid fusion method is proposed and its implementation is described. Experimental results indicate that hybrid fusion method has better performance than existed methods and it can solve Double Low problem to some degree. Also it can be used as a framework to fuse different biometrics to achieve Double Low goal. It has considerable value for high security applications in biometrics field. Future Scope To fuse more different fingerprint algorithms with the proposed hybrid fusion method to solve the Double Low problem more thoroughly for fingerprint identification. To try the proposed hybrid fusion method with different biometrics for high security applications. Applications Banking Security - ATM security, card transaction Physical Access Control (e.g. Airport) Information System Security National ID Systems Passport control Prisoner, prison visitors Voting Identification of Criminals Secure E-Commerce

8 162 Kawade Sonam P & V. S. Ubale REFERENCES 1. Yilong Yin, Yanbin Ning and Zhiguo Yang, A HYBRID FUSION METHOD OF FINGERPRINT IDENTIFICATION FOR HIGH SECURITY APPLICATIONS, Proceedings of 2010 IEEE 17th International Conference on Image Processing, September 26-29, 2010, Hong Kong. Page No Le Hoang Thai and Ha Nhat Tam, Fingerprint recognition using standardized fingerprint model, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 7, May 2010, Page No