The human hand has been considered a promising
component for biometric-based identification and authentication
systems for many decades. In this paper, hand side recognition
framework is proposed based on deep learning and biometric
authentication using the hashing method. The proposed approach
performs in three phases: (a) hand image segmentation and
enhancement by morphological filtering, automatic thresholding,
and active contour deformation, (b) hand side recognition based
on deep Convolutional Neural Networks (CNN), and (c) biometric
authentication based on the hashing method. The proposed
framework is evaluated using a very large hand dataset, which
consists of 11076 hand images, including left/ right and dorsal/
palm hand images for 190 persons. Finally, the experimental
results show the efficiency of the proposed framework in both
dorsal-palm and left-right recognition with an average accuracy
of 96.24 and 98.26, respectively, using a completely automated
computer program.