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Concept: Fingerprint Verification Competition


The performance of conventional minutiae-based fingerprint authentication algorithms degrades significantly when dealing with low quality fingerprints with lots of cuts or scratches. A similar degradation of the minutiae-based algorithms is observed when small overlapping areas appear because of the quite narrow width of the sensors. Based on the detection of minutiae, Scale Invariant Feature Transformation (SIFT) descriptors are employed to fulfill verification tasks in the above difficult scenarios. However, the original SIFT algorithm is not suitable for fingerprint because of: (1) the similar patterns of parallel ridges; and (2) high computational resource consumption. To enhance the efficiency and effectiveness of the algorithm for fingerprint verification, we propose a SIFT-based Minutia Descriptor (SMD) to improve the SIFT algorithm through image processing, descriptor extraction and matcher. A two-step fast matcher, named improved All Descriptor-Pair Matching (iADM), is also proposed to implement the 1:N verifications in real-time. Fingerprint Identification using SMD and iADM (FISiA) achieved a significant improvement with respect to accuracy in representative databases compared with the conventional minutiae-based method. The speed of FISiA also can meet real-time requirements.

Concepts: Algorithm, Fingerprint Verification Competition, Better, Biometrics, Scale-invariant feature transform, Computational complexity theory, Improve, Fingerprint


OBJECTIVES To determine the prevalence of fingerprint verification failure and to define and quantify the fingerprint changes associated with fingerprint verification failure. DESIGN Case-control study. SETTING Referral public dermatology center. PATIENTS The study included 100 consecutive patients with clinical hand dermatitis involving the palmar distal phalanx of either thumb and 100 age-, sex-, and ethnicity-matched controls. Patients with an altered thumbprint due to other causes and palmar hyperhidrosis were excluded. MAIN OUTCOME MEASURES Fingerprint verification (pass/fail) and hand eczema severity index score. RESULTS Twenty-seven percent of patients failed fingerprint verification compared with 2% of controls. Fingerprint verification failure was associated with a higher hand eczema severity index score (P < .001). The main fingerprint abnormalities were fingerprint dystrophy (42.0%) and abnormal white lines (79.5%). The number of abnormal white lines was significantly higher among the patients with hand dermatitis compared with controls (P = .001). Among the patients with hand dermatitis, the odds of failing fingerprint verification with fingerprint dystrophy was 4.01. The presence of broad lines and long lines was associated with a greater odds of fingerprint verification failure (odds ratio [OR], 8.04; 95% CI, 3.56-18.17 and OR, 2.37; 95% CI, 1.31-4.27, respectively), while the presence of thin lines was protective of verification failure (OR, 0.45; 95% CI, 0.23-0.89). CONCLUSIONS Fingerprint verification failure is a significant problem among patients with more severe hand dermatitis. It is mainly due to fingerprint dystrophy and abnormal white lines. TRIAL REGISTRATION Malaysian National Medical Research Register Identifier: NMRR-11-30-8226.

Concepts: Case-control study, Failure, Fingerprint Verification Competition, Hand, Epidemiology, Finger, Fingerprint, Eczema


Establishing correspondences between two minutia sets is a fundamental issue in fingerprint recognition. This paper proposes a new tensor matching strategy. First, the concept of minutia tensor matrix (simplified as MTM) is proposed. It describes the first-order features and second-order features of a matching pair. In the MTM, the diagonal elements indicate similarities of minutia pairs and non-diagonal elements indicate pairwise compatibilities between minutia pairs. Correct minutia pairs are likely to establish both large similarities and large compatibilities, so they form a dense sub-block. Minutia matching is then formulated as recovering the dense sub-block in the MTM. This is a new tensor matching strategy for fingerprint recognition. Second, as fingerprint images show both local rigidity and global nonlinearity, we design two different kinds of MTMs: local MTM and global MTM. Meanwhile, a two-level matching algorithm is proposed. For local matching level, the local MTM is constructed and a novel local similarity calculation strategy is proposed. It makes full use of local rigidity in fingerprints. For global matching level, the global MTM is constructed to calculate similarities of entire minutia sets. It makes full use of global compatibility in fingerprints. Proposed method has stronger description ability and better robustness to noise and nonlinearity. Experiments conducted on Fingerprint Verification Competition databases (FVC2002 and FVC2004) demonstrate the effectiveness and the efficiency.

Concepts: Proposal, Similarity, Algorithm, Calculation, Mathematics, Fingerprint Verification Competition, Biometrics, Fingerprint