지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2012.11
- 수록면
- 242 - 246 (5page)
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초록· 키워드
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Objective: The aim of this study is to build an emotion recognition system that recognizes emotion on the basis of human facial expressions. Background: Emotion recognition is important for intelligent UI (User Interface) of computers. Our approach is to combine insights gained from psychological research and the power of k-NN (k Nearest Neighbors) and SVM (Support Vector Machine) to recognize facial emotions. Method: Our dataset was still images that recorded people’s facial expressions at their peak in 3 emotion-inducing situations. The emotions were: "joy", "anger", and "disgust". Using ASM (Active Shape Model), we extracted geometric features from recorded videos. The selection of features was based on findings of previous research. For k-NN, when a new image is presented, the model finds k most similar instances from the training data and recognizes an emotion that was most often associated with the k instances. For SVM, we trained 3 SVMs (joy vs. anger, anger vs. disgust, disgust vs. joy) to discriminate the geometric patterns of facial expressions. Then we presented a new image to the models to classify a most likely emotion category. Results: LOOCV (Leave-One-Out Cross Validation) was used to evaluate the performance. The accuracy of correct classification was 96% and 99% for k-NN and SVM, respectively. Conclusion: Our facial emotion recognition system that uses k-NN and SVM to classify temporal patterns of facial expressions achieved a very high rate of emotion detection. Application: Our emotion recognition system can be used to build more intelligent user interfaces.
목차
ABSTRACT
1. Introduction
2. Method
3. Results
4. Conclusion
References
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