Palm print 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 mode 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, contactlessdevices 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 a ROI extraction method that addresses this issue.We test our method on two databases PolyU and CASIA which 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.

Several biometric threats systems models have been proposed to facilitate the design, implementation and validation techniques for securing these systems. Some models classify threats by type of attacks, others by specific attacks and other by using vulnerabilities and threat agent. Each model proposes a vision and a different approach to identify these threats. For example, to design security techniques for wireless biometric card, one should identify all threats facing this kind of device. In this paper, a comparative study and synthesis to help choose the most fitting model, depending on the security problems addressed, is given.