Authentication through the palmprint is a field of biometrics. Palmprint-based personal verification has quickly entered the biometric family. It has become increasingly popular in the recent years due to its ease of acquisition, reliability and high user acceptance. In this paper, we present an authentication system based on the palmprint. We are particularly interested in the feature extraction step. Three feature extraction techniques based on the discrete wavelet transform, the Gabor filters and the co-occurrence matrix are evaluated. The support vector machine is used for the classification step. The results have been validated on the PolyU database related to 400 users. The best results have been achieved with the wavelet decomposition.

In multimodal biometrics, modalities can be robust against the authentication of certain people and weak for others. The conventional fusion techniques such as the Product, Mean, AND, OR and the Majority Voting do not take into account this kind of behaviour. In this paper, we propose a new approach to fusion procedures in the context of biometric authentication. The proposed method is based on the exploration of the Choquet integral that takes into account the interactions between the terms and people through fuzzy measures. The fuzzy measures, the ones we have proposed, are based on the number of confusion, the entropy and the uncertainty function. The results have been validated in two databases: the first one is virtual, which is based on synthetic scores and the second one on the biometric modalities which are: face, off-line handwriting and off-line signature. The achieved results demonstrate the robustness of our approaches and their adaptability.