Bimodal Biometric Method Fusing Hand Shape and Palmprint Modalities at Rank Level

Person identification becomes increasingly an important task to guarantee the security of persons with the possible fraud attacks, in our life. In this paper, we propose a bimodal biometric system based on hand shape and palmprint modalities for person identification. For each modality, the SIFT descriptors (Scale Invariant Feature Transform) are extracted thanks to their advantages based on the invariance of features to possible rotation, translation, scale and illumination changes in images. These descriptors are then represented sparsely using sparse representation method. The fusion step is carried out at rank level after the classification step using SVM (Support Vector Machines) classifier, in which matching scores are transformed into probability measures. The experimentation is performed on the IITD hand database and results demonstrate encouraging performances achieving IR=99.34% which are competitive to methods fusing hand shape and palmprint modalities existing in the literature.