title: Facial emotion recognition system for autistic children: a feasible study based on FPGA implementation.
abstract: Children with autism spectrum disorder have difficulty in understanding the emotional and mental states from the facial expressions of the people they interact. The inability to understand other people's emotions will hinder their interpersonal communication. Though many facial emotion recognition algorithms have been proposed in the literature, they are mainly intended for processing by a personal computer, which limits their usability in on-the-move applications where portability is desired. The portability of the system will ensure ease of use and real-time emotion recognition and that will aid for immediate feedback while communicating with caretakers. Principal component analysis (PCA) has been identified as the least complex feature extraction algorithm to be implemented in hardware. In this paper, we present a detailed study of the implementation of serial and parallel implementation of PCA in order to identify the most feasible method for realization of a portable emotion detector for autistic children. The proposed emotion recognizer architectures are implemented on Virtex 7 XC7VX330T FFG1761-3 FPGA. We achieved 82.3% detection accuracy for a word length of 8 bits.
authors.affiliation: School of Computer Engineering, Nanyang Technological University, Singapore, Singapore. smitha@ntu.edu.sg.
authors.forename: K G
authors.initials: KG
authors.lastname: Smitha
authors.affiliation: School of Computer Engineering, Nanyang Technological University, Singapore, Singapore. asvinod@ntu.edu.sg.
authors.forename: A P
authors.initials: AP
authors.lastname: Vinod
mesh_terms: D000465:Algorithms; D001321:Autistic Disorder / Q000534:rehabilitation*; D004644:Emotions / Q000145:classification*; D005149:Facial Expression; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis
keywords: FPGA implementation; Facial emotion recognition; Real-time and portability