International Journal of Advance Computational Engineering and Networking (IJACEN)
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Statistics report
Feb. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 83
Paper Published : 1216
No. of Authors : 3056
  Journal Paper

Paper Title
Depth Face Recognition through Deep Learning Networks Fine-Tuning

Abstract
Face recognition still has many challenges, and with the appearance of the Microsoft Kinect device, new potentials of research were discovered, trying to use the Kinect depth maps as a data source to recognize human faces was considered an interesting research area, mainly because the Kinect could provide the required data in an affordable and accurate way, but till this day no research managed to utilize the new and popular deep learning techniques to achieve higher accuracy and better results on face recognition using any deep learning technique and Kinect 2 depth map images. In this paper, with the goal of enhancing face recognition through the use of depth map images and the lack of a depth map datasets that can be used to train a deep learning network, we introduce a new practice for network fine-tuning, as the methodologies were applied with face depth maps to achieve high face recognition accuracy. The process of building a convolutional neural network and loading weights of a similar Image-net trained network was introduced, where the network was fine-tuned and trained to work with depth map images of faces. The results of the new training procedure presented superior performance compared to any previous methods where depth maps were the source of data. Keywords- Face recognition; Kinect 2; Deep Learning; Fine tuning, Convolutional neural network.


Author - Yaser Saleh

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| Published on 2018-08-28
   
   
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