Multi-tasking deep convolutional network architecture design for extracting nonverbal communicative information from a face |
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| Institution: | 1. School of Electronical and Electronics Engineering, Chung-Ang University, 84, Heukseok-Ro, Dongjak-Gu, Seoul 06974, Republic of Korea;2. Korea Institute of Industrial Technology, 143 Hanggaulro, Sangnok-gu, Ansan-si, Gyeonggi-do 15588, Republic of Korea;1. School of Economics and Management, East China Jiaotong University, Nanchang, China;2. School of Business, Guangdong University of Foreign Studies, Guangzhou, China;1. School of Computer Science, Northwestern Polytechnical University, Xi''an, China;2. School of Computer Science, Xi''an University of Technology, Xi''an, China;3. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, China;4. Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK;5. College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, China;1. Department of Computer Science, Kitami Institute of Technology, 165 Koen-cho, Kitami 090-8507, Japan;2. Naoto Ishii graduated the Department of Computer Science, Kitami Institute of Technology in March 2018, Japan |
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| Abstract: | Facial expressions convey not only emotions but also communicative information. Therefore, facial expressions should be analysed to understand communication. The objective of this study is to develop an automatic facial expression analysis system for extracting nonverbal communicative information. This study focuses on specific communicative information: emotions expressed through facial movements and the direction of the expressions. We propose a multi-tasking deep convolutional network (DCN) to classify facial expressions, detect the facial regions, and estimate face angles. We reformulate facial region detection and face angle estimation as regression problems and add task-specific output layers in the DCN’s architecture. Experimental results show that the proposed method performs all tasks accurately. In this study, we show the feasibility of the multi-tasking DCN for extracting nonverbal communicative information from a human face. |
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| Keywords: | Neural network Facial expression Nonverbal communication |
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