Palmprint Recognition Based on Complete Direction Representation.

Direction information serves as one of the most important features for palmprint recognition. In the past decade, many effective direction representation (DR)-based methods have been proposed and achieved promising recognition performance. However, due to an incomplete understanding for DR, these methods only extract DR in one direction level and one scale. Hence, they did not fully utilized all potentials of DR. In addition, most researchers only focused on the DR extraction in spatial coding domain, and rarely considered the methods in frequency domain. In this paper, we propose a general framework for DR-based method named Complete Direction Representation (CDR), which reveals DR by a comprehensive and complete way. Different from traditional methods, CDR emphasizes the use of direction information with strategies of multi-scale, multi-direction level, multi-region, as well as feature selection or learning. This way, CDR subsumes previous methods as special cases. Moreover, thanks to its new insight, CDR can guide the design of new DR-based methods toward better performance. Motived this way, we propose a novel palmprint recognition algorithm in frequency domain. Firstly, we extract CDR using multi-scale modified finite radon transformation (MFRAT). Then, an effective correlation filter, namely Band-Limited Phase-Only Correlation (BLPOC), is explored for pattern matching. To remove feature redundancy, the Sequential Forward Selection (SFS) method is used to select a small number of CDR images. Finally, the matching scores obtained from different selected features are integrated using score-level-fusion. Experiments demonstrate that our method can achieve better recognition accuracy than the other state-of-the-art methods. More importantly, it has fast matching speed, making it quite suitable for the large-scale identification applications.