Transfer learning using Alex Net Convolutional Neural Network for Face Recognition
Ridza Azri Bin Ramlee1, Yvonne Yap2, Siva Kumar Subramaniam3, Mohamad Harris Misran4, Asem Khmag5
1Ridza Azri Bin Ramlee, FKEKK, UTeM, Hang Tuah Jaya, Durian Tunggal, Melaka, Malaysia.
2Yvonne Yap, Centre for Telecommunication Research & Innovation, UTeM, Hang Tuah Jaya, Durian Tunggal, Melaka, Malaysia.
3Siva Kumar Subramaniam, Centre for Telecommunication Research & Innovation, UTeM, Hang Tuah Jaya, Durian Tunggal, Melaka, Malaysia.
4Mohamad Harris Misran, Centre for Telecommunication Research & Innovation, UTeM, Hang Tuah Jaya, Durian Tunggal, Melaka, Malaysia.
5Asem Khmag, Department of Computer Systems Engineering University of Zawia, Libya.
Manuscript received on August 15, 2020. | Revised Manuscript received on August 23, 2020. | Manuscript published on September 10, 2020. | PP: 285-294 | Volume-9 Issue-11, September 2020 | Retrieval Number: 100.1/ijitee.K77760991120 | DOI: 10.35940/ijitee.K7776.0991120
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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: This research is aimed to achieve high-precision accuracy and for face recognition system. Convolution Neural Network is one of the Deep Learning approaches and has demonstrated excellent performance in many fields, including image recognition of a large amount of training data (such as ImageNet). In fact, hardware limitations and insufficient training data-sets are the challenges of getting high performance. Therefore, in this work the Deep Transfer Learning method using Alex Net pre-trained CNN is proposed to improve the performance of the face-recognition system even for a smaller number of images. The transfer learning method is used to fine-tuning on the last layer of Alex Net CNN model for new classification tasks. The data augmentation (DA) technique also proposed to minimize the over-fitting problem during Deep transfer learning training and to improve accuracy. The results proved the improvement in over-fitting and in performance after using the data augmentation technique. All the experiments were tested on UTeMFD, GTFD, and CASIA-Face V5 small data-sets. As a result, the proposed system achieved a high accuracy as 100% on UTeMFD, 96.67% on GTFD, and 95.60% on CASIA-Face V5 in less than 0.05 seconds of recognition time.
Keywords: CNN, Data Augmentation, Face Recognition, Transfer Learning.
Scope of the Article: Deep Learning