Abstract : Including multiple sources of information in personal identity recognition and verification gives the opportunity to greatly improve performance. In this paper, a contactless biometric system is proposed, which combines two modalities: palmprint and face. Hardware implementations are proposed on DSP and FPGA platforms. The algorithmic chain consists of a preprocessing (which includes palm extraction from hand images), Gabor feature extraction, comparison by Hamming distance, and score fusion. Fusion possibilities are discussed and tested using first a bimodal database of 130 subjects designed by the authors (uB database), and then two common public biometric databases (AR for face and PolyU for palmprint). High performance has been obtained for recognition and verification purpose: a recognition rate of 97.49% with AR-PolyU database and an equal error rate of 1.10% on the uB database using only 2 training samples per subject have been obtained. Hardware results demonstrate that preprocessing can easily be performed during the acquisition phase, and multimodal biometric recognition can be treated almost instantly (0.4 ms on FPGA). This study shows the feasibility of a robust and efficient multimodal hardware biometric system which offers several advantages such as user-friendliness and flexibility.