Palmprint Recognition System: Case Study With Different Databases

Palmprint is one of the modalities that offer high recognition accuracy. The recognition process depends on an optimized ROI (Region of Interest) extraction. This extraction is affected by several factors including the device used and the acquisition conditions. The acquisition step can alter some image properties like rotation, translation and scale. Some devices are designed to maintain hand in a fixed position and delimit a subspace of the hand. On the other hand, contactless devices offer more convenience and flexibility but lead to altered images. ROI extraction methods must consider the acquisition device (with contact or contactless). In this paper, we propose palmprint recognition system considering this issue. A ROI extraction method is implemented and tested on different databases taking into account the acquisition device with different sensor conditions. Our research to test this method, are conducted on two databases PolyU(1) (collected by the Polytechnique University PolyU) and CASIA(2) (collected by the Chinese Academy of Sciences' Institute of Automation (CASIA)) that illustrate the impact of using contactless device unlike the PolyU device. Then, we test performances of the palmprint biometric system. We use a Fisher Linear Discriminant projection (FLD) to extract features from ROI transformed into the frequency domain. Our proposed method can significantly cover a great portion of the palm in the two databases. Performances obtained with the proposed palmprint system are promising.