No abstract

Phase-based image matching has shown high recognition accuracy in palmprint verification. The algorithm compares a pair of palmprint images by extracting local phase features from the images and computing local correlation functions between them. A major drawback of this algorithm is its high computational cost associated with the evaluation of local correlation functions. This needs to be addressed, especially in the case of one-to-many comparisons required for palmprint identification. The problem becomes increasingly severe as the number of enrolled images increases. In this paper, we propose a novel palmprint identification algorithm with low computational complexity, which employs a sparse representation of enrolled phase features (i.e., phase templates) to evaluate local correlation functions. For this purpose, we also develop an efficient Convolutional Sparse Coding (CSC) algorithm that can derive a compact representation of phase templates. The proposed method reduces the computational cost of phase-based palmprint identification without significant degradation of recognition performance. Our experiments using public databases clearly demonstrate the advantage of the proposed method over conventional methods.

Access control and surveillance applications like walkingthrough security gates and immigration control points have a great demand for convenient and accurate biometric recognition in unconstrained scenarios with low user cooperation. The periocular region, which is a relatively new biometric trait, has been attracting much attention for recognition of an individual in such scenarios. This paper proposes a periocular recognition method that combines Phase-Based Correspondence Matching (PB-CM) with a texture enhancement technique. PB-CM has demonstrated high recognition performance in other biometric traits, e.g., face, palmprint and finger-knuckle-print. However, a major limitation for periocular region is that the performance of PB-CM degrades when the periocular skin has poor texture. We address this problem by applying texture enhancement and found out that variance normalization of texture significantly improves the performance of periocular recognition using PB-CM. Experimental evaluation using three public databases demonstrates the advantage of the proposed method compared with conventional methods.