Hand Side Recognition and Authentication System based on Deep Convolutional Neural Networks
Mohammad Abbadi1, Afaf Tareef2, Afnan Sarayreh3

1Mohammad Abbadi, Computer Science from George Washington University, USA.
2Afaf Tareef, Department of Computer Science from Mutah University, Jordan.
3Afnan Sarayreh, Department of Computer Science from Mutah University, Jordan.

Manuscript received on January 06, 2021. | Revised Manuscript received on January 11, 2021. | Manuscript published on February 28, 2021. | PP: 5-13 | Volume-10 Issue-4, February 2021 | Retrieval Number: 100.1/ijitee.D84300210421 | DOI: 10.35940/ijitee.D8430.0210421
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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. 
Keywords: Hand side recognition, Biometric authentication, Deep learning, Automatic ROI segmentation, Convolutional neural networks, Hashing function.