We propose a new hybrid protocol for cryptographically secure biometric authentication. The main advantages of the proposed protocol over previous solutions can be summarised as follows: (1) potential for much better accuracy using different types of biometric signals, including behavioural ones; and (2) improved user privacy, since user identities are not transmitted at any point in the protocol execution. The new protocol takes advantage of state-of-the-art identification classifiers, which provide not only better accuracy, but also the possibility to perform authentication without knowing who the user claims to be. Cryptographic security is based on the Paillier public key encryption scheme.

/pdf/secure-biometric-authentication-with-improved-accuracy-1ougiurl3v.pdf

Secure Biometric Authentication with Improved Accuracy

Secure Biometric Authentication With Improved Accuracy.

Fingerprint classification and matching are two key issues in automatic fingerprint recognition. Generally, fingerprint recognition is based on a set of relevant local characteristics, such as ridge ending and bifurcation (minutiae). Fingerprint classification is based on fingerprint global features, such as core and delta singularity points. Unfortunately, singularity points are not always present in a fingerprint image: the acquisition process is not ideal, so that the fingerprint is broken, or the fingerprint belongs to the arch class. In the above cases, pseudo-singularity-points will be detected and extracted to make possible fingerprint classification and matching. As result, fingerprint processing involves few steps and, in the same way, fingerprint matching involves the comparison of few features with recognition rates comparable to the standard minutiae based systems. The experiments trials have been conducted on many official Fingerprint Verification Competition (FVC) databases. The achieved results show the effectiveness of the proposed approach, obtaining a False Acceptance Rate (FAR) = 1.22% and a False Rejection Rate (FRR) = 9.23% with FVC2002 DB2-A database. In the best of case, a FAR=0.26% and a FRR=7.36% with FVC2000 DB1-B database is achieved. To the best of our knowledge, this is the first recognition system based only on singularity regions.

Introducing Pseudo-Singularity Points for Efficient Fingerprints Classification and Recognition

Person identification and recognition using fingerprints as a biometric authentication is one of the unique, prominent and reliable method of verification processes. The paper presents fingerprint authentication system using filter bank based matching algorithm. The algorithm uses Gabor filter bank to extract features of fingerprints. These features are then compared for identification and recognition of a person. The Euclidian distance is used to compare feature vector of query image with feature vectors of database fingerprints. Two sets of databases FVC2000 and DBIT are used for evaluating the performance and efficiency of the algorithm. The experimental results show that the average efficiency of the algorithm is 82.95% and 89.68% for FVC2000 and DBIT fingerprint databases respectively.

Fingerprint authentication using Gabor filter based matching algorithm

Research highlights? This paper proposes biometric-based fractal pattern classifier for fingerprint recognition. ? Biometric characteristics are extracted as fractal patterns using Weierstrass cosine function with different fractal dimensions. ? Grey relational analysis is used to determine the relational grade between a given reference pattern and a given set of comparative patterns. ? For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition. This paper proposes biometric-based fractal pattern classifier for fingerprint recognition using grey relational analysis (GRA). Fingerprint patterns have arch, loop, whorl, and accidental morphologys, and embed singular points, which result in establishing fingerprint individuality. An automatic fingerprint identification system consists of three stages: image acquisition and processing, feature extraction, and pattern recognition. Fingerprint images are captured from subjects using an optical fingerprint reader (OFR). Digital image preprocessing (DIP) is used to refine out noise, enhance the image, convert to binary image, and locate the reference point. For binary images, Katz's algorithm is employed to estimate the fractal dimension (FD) from two-dimension (2D) image. Biometric characteristics are extracted as fractal patterns using Weierstrass cosine function (WCF) with different FDs. GRA performs to compare the fractal patterns among the small-scale database. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.

Optical sensor measurement and biometric-based fractal pattern classifier for fingerprint recognition

Fingerprint identification system is mainly consisted of fingerprint achieving, fingerprint classification and fingerprint matching. Fingerprint matching is the key to the system and effects on the precision and efficiency of the whole system directly. Fingerprints are matched mainly based on their fingerprint texture pattern, which can be described with the orientation field of fingerprints. A fingerprint, which has the different orientation angle structure in different local area of the fingerprint and has a texture pattern correlation among the neighboring local areas of the fingerprint, can be viewed as a Markov stochastic field. A novel method of fingerprint matching, which is based on embedded hidden Markov model (HMM) that is used for modeling the fingerprint's orientation field, is described in this paper. The accurate and robust fingerprint matching can be achieved by matching embedded hidden Markov model parameters which were built after the processing of extracting features from a fingerprint, forming the samples of observation vectors and training the embedded hidden Markov model parameters.

A hidden Markov model fingerprint matching approach

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A hidden Markov model fingerprint matching approach