Palmprint recognition based on discriminant multiscale representation

Nowadays, important scientific works are oriented to develop optimal palmprint representations, especially used in the palmprint recognition field. These representations must be discriminant, robust, compact, and easy-to-implement in order to ensure the best performance in terms of accuracy, computation cost, and storage requirement. In this way, our paper presents a multiscale analysis framework for efficient palmprint representation called discriminant Gabor Lpq spatial pyramid histogram, which significantly relies on Gabor wavelet, local phase quantization (LPQ) descriptor, and spatial pyramid histogram (SPH) method. First, the Gabor wavelet function with two scales and four orientations is used to capture the local structure in palmprint images. Second, the LPQ operator is applied to the response images of Gabor filter to get label LPQ images in order to fully explore the blur invariant property and the texture information. This is conducted in multiscale and multidirection space. Third, for each of them, the SPH of vertical decomposition is applied to obtain local palmprint feature descriptors. Next, the obtained histograms are normalized. Then the global representation of the palmprint image is obtained by concatenating all the local feature descriptors. After that, the discriminant representation of the palmprint image is constructed using whitened linear discriminant analysis. Finally, the K-nearest neighbor classifier is used for identification. Experiments, conducted on three palmprint databases (PolyU2D, PolyU 2D/3D, and IITD), show that the proposed method provides a significant performance improvement compared to the state-of-the-art and recently proposed methods in terms of accuracy.