Multiscale Multidirection Binary Pattern Learning for Discriminant Palmprint Identification

Abstract

Binary pattern learning has been achieved promising recognition performance in palmprint identification. However, most of direction representation-based methods are hand-crafted. They present the direction characteristics only in one scale, and thus the comprehensive information from multiple scales and multiple directions are ignored. This is also true especially for the binary coding-based methods. As a solution to the problem, this paper proposes a multi-scale multi-direction binary (MSMDB) pattern learning method for palmprint identification. Specifically, we first form a feature container of multi-scales multi-directions for palmprint images. The container contains robust direction average vectors (DAVs). Then, a learning model is proposed to learn robust and discriminant direction information from the extracted convolution average features. The proposed model can project these DAVs into discriminant direction binary codes. The model not only maximizes the variance of the learned binary codes and inter-class distance, but also minimizes intra-class distance. Finally, we cluster all block histograms of multi-scale projections to form the discriminative direction binary palmprint descriptors for palmprint identification. We evaluate the performance of the proposed method on five public databases compared to other related methods. For example, MSMDB can achieve the best identification accuracy of 97.93 percent when the number of training data is 2 on the database IITD.