Learning binary hash codes for finger vein image retrieval

Abstract

High dimensional real-valued features have been shown to be effective for finger vein identification, but result in high computational cost especially in a large-scale finger vein database. Therefore compact binary codes are generally used to obtain fast query speed as well as reduce storage requirements. However, only a few strategies are available for finger vein retrieval in Hamming space, and existing binary representation of finger veins can become unstable and undiscriminating due to finger vein variations induced by translation, rotation and illumination. In this paper, we propose a binary hash codes learning algorithm to map finger vein images in the original feature space to Hamming space. First, to obtain the discriminative finger vein image features, a novel finger vein image representation method called Nonlinearly Subspace Coding (NSC) is proposed. The codebook is a union of low-dimensional linear subspaces instead of visual words. For a given local finger vein texton, its top-k nearest subspaces are found and the texton is nonlinearly mapped into these subspaces. Then, a finger vein Binary Hash Codes (BHC) learning method is proposed by jointly considering the discriminability and the stability of the binary code. Experimental results on a two-session public finger vein database and a large fused finger vein database demonstrate the effectiveness and efficiency of our binary hash coding learning algorithm for large-scale finger vein retrieval.