Towards Effective 2D and 3D Palmprint Recognition Using Transfer Learning Deep Features and RelifF method

Recently, 3D palmprint recognition systems have started to gain the attention of researchers compared to their 2D counterpart. The key task in the design of a biometric identification system is the choice of the method of extracting discriminating information from 2D/3D images. Given the enormous success of deep learning approaches in the field of feature extraction and classification, we propose in this paper the use of Transfer learning networks in order to build a palmprint based person identification system. Also, this paper aims to show the efficiency of feature selection to improve system performance. To do this, a Relief method was used to perform the feature selection task. Experiments on a 2D/3D palmprint database with 8000 samples show that the proposed scheme can significantly improve the efficiency of palmprint identification.