Emotion Recognition using Adaptive Motion Analysis on Facial Features
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In this paper, we present facial expression recognition using feature-adaptive motion analysis and ID3 decision tree. Our method optimized the information gain heuristics of ID3 and consists of three parts: (1) simple geometric face model representation, (2) effective facial feature extraction using feature selection and feature-adaptive motion analysis, and (3) computationally inexpensive rule-based classification using ID3 tree. Our methods avoids complicated face model representation such as 3D modeling of human face, rather it uses FAC5-alike but much simpler face model by using fewer features and their actions. Feature selection and feature-adaptive motion analysis estimate motion patterns in fast and effective manner by assigning computational complexity on necessary parts only. Moreover, information gain heuristics of ID3 tree forces the classification to be done with minimal Boolean comparison. The performance was acceptable with overall recognition accuracy of 77% for JAFFE database (95 expressed images).
Abstract
In this paper, we present facial expression recognition using feature-adaptive motion analysis and ID3 decision tree. Our method optimized the information gain heuristics of ID3 and consists of three parts: (1) simple geometric face model representation, (2) effective facial feature extraction using feature selection and feature-adaptive motion analysis, and (3) computationally inexpensive rule-based classification using ID3 tree. Our methods avoids complicated face model representation such as 3D modeling of human face, rather it uses FAC5-alike but much simpler face model by using fewer features and their actions. Feature selection and feature-adaptive motion analysis estimate motion patterns in fast and effective manner by assigning computational complexity on necessary parts only. Moreover, information gain heuristics of ID3 tree forces the classification to be done with minimal Boolean comparison. The performance was acceptable with overall recognition accuracy of 77% for JAFFE database (95 expressed images).