Palmprint recognition using fusion of local and global features

Abstract

Palmprint recognition is a rapidly developing biometrics technology over the last decade. However, there exist some typical problems when capturing palmprint images. First, the delta region in the center palm will raise the uneven light and brightness of the palmprint images varying with hand pressure, stretching and palm structure. Second, it is hard to align the palmprint images precisely to the same position, especially when the subjects are required to spread their hand on the scanner surface, even for the same palm. Either the global or the local features cannot satisfy the need for high recognition accuracy. Therefore, we propose a novel method using fusion of local and global features, extracted by non-negative factorization with sparseness constraint (NMFsc) and prominent component analysis (PCA), respectively, to improve the recognition performance. Experiments demonstrate the strong supplementary between local and global features for palmprint recognition.