Palmprint verification using binary contrast context vector

Palmprint recognition has attracted much attention in recent years. Many algorithms based texture coding achieve high accuracy. However they are still sensitive to local unsteady region introduced by variations of hand pose and other conditions. In this paper we proposed a novel feature extraction algorithm, namely binary contrast context vector (BCCV), to represent multiple contrast distribution for a local region. Due to forming the local contrast value into a binary vector, contrast context could be used to match more effectively. Furthermore, by using BCCV we apply an adaptive threshold to mask the stable local region before matching. Our experiment results on public palmprint database shows that the proposed BCCV achieves lower equal error rate (EER) than other two state-of-the-art approaches.