3D-Based Facial Emotion Recognition using Depthwise Separable Convolution

Author:

Abubakar Hassan Sani1,Hossin MD Altab2,Yussif Sophyani Banaamwini3,Fargalla Mandela Ali Margan4,Rashid Ramadhan Said5,Umar Yusuf Jamilu6,Chima Ukwuoma Chiagoziem7,Kuupole Ewald Erubaar8

Affiliation:

1. School of Information and Communication Engineering,Medialab, University of Electronics Science and Technology of ChinaChengdu, Sichuan, China, China

2. School of Management and Economics, University of Electronics Science and Technology of ChinaChengdu, Sichuan, China, China

3. School of Computer Science and Engineering, University of Electronics Science and Technology of ChinaChengdu, Sichuan, China, China

4. Institute of Unconventional Petroleum Science and Technology, China University of Petroleum Beijing,, China

5. School of life Science, University of Electronics Science and Technology of ChinaChengdu, Sichuan, China, China

6. School of Mathematics, Central South University Changsha, China

7. School of Information and Software Engineering, University of Electronics Science and Technology of ChinaChengdu, Sichuan, China, China

8. Directorate of ICT Services, University of Technology and Applied SciencesNavrongo, Ghana

Publisher

ACM

Reference29 articles.

1. The Experience of Emotion

2. A Set of Selected SIFT Features for 3D Facial Expression Recognition

3. 3D Object Recognition Method Based on Point Cloud Sequential Coding

4. Tianhong Fang , Xi Zhao , Omar Ocegueda , Shishir  K. Shah , and Ioannis  A. Kakadiaris . 2012. 3D/4 D facial expression analysis : An advanced annotated face model approach. Image and vision Computing. 30, 10 ( 2012 ), 738–749. Tianhong Fang, Xi Zhao, Omar Ocegueda, Shishir K. Shah, and Ioannis A. Kakadiaris. 2012. 3D/4D facial expression analysis: An advanced annotated face model approach. Image and vision Computing. 30, 10 (2012), 738–749.

5. FERLrTc: 2D+3D facial expression recognition via low-rank tensor completion

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