Palmprint based recognition system using phase-difference information

This paper presents an efficient palmprint based human recognition system. Each palmprint is divided into several square overlapping blocks. Reconstruction error using principle component analysis (PCA) is used to classify these blocks into either a good block or a non-palmprint block. Features from each good block of a palmprint are obtained by binarising the phase-difference of vertical and horizontal phase. The Hamming distance is used to compute the matching score between the features of corresponding good blocks of enrolled and live palmprint. These matching scores are fused using weighted sum rule, where weights are based on the average discriminating level of a block relative to other blocks. The performance of the proposed system is analysed on different datasets of hand images and it has been observed that it achieves a Correct Recognition Rate of 100% with a low Equal Error Rate for all the datasets. The system is also evaluated for noisy and bad palmprint images. It is found to be robust as long as the noise density is less than 50% or the bad region is less than 64% of the images.

Research highlights
► An efficient palmprint based human recognition system is proposed.
► A proposed technique is based using local phase-difference features of palmprints.
► The system is tested for performance on three different datasets of hand images.
► The system is also evaluated for noisy and bad palmprint images.
► The proposed system achieves a very high Recognition Rate and low Equal Error Rate.