PALMPRINT RECOGNITION USING COARSE-TO-FINE STATISTICAL IMAGE REPRESENTATION

Recent literatures have revealed that statistics of local texture measures can provide accurate descriptions of palmprint appearances. In this framework, one palmprint image is divided into local blocks with multiple spatial resolutions. The statistical texture descriptions of each block are then concatenated to form a multi-scale image representation. However, resultant high-dimensional statistical features lead to increasing of computational cost. In this paper, we tackle this problem by performing a coarse-to-fine cascade scheme, which makes use of information redundancy of statistical texture descriptions between different spatial scales. In contrast with non-cascade strategies, the proposed method reduces most of computational burden and achieves accurate classification simultaneously.