상세정보
학술지
얼굴 표정 인식을 위한 방향성 LBP 특징과 분별 영역 학습
- 발행일
- 201705
- 출처
- 멀티미디어학회논문지, v.20 no.5, pp.748-757
- ISSN
- 1229-7771
- 출판사
- 한국멀티미디어학회
- 협약과제
- 17ZD1100, 대경권 지역산업연계 IT융합기술개발 및 산업계 지원사업, 문기영
- 초록
- In order to recognize the facial expressions, good features that can express the facial expressions are essential. It is also essential to find the characteristic areas where facial expressions appear discriminatively. In this study, we propose a directional LBP feature for facial expression recognition and a method of finding directional LBP operation and feature region for facial expression classification. The proposed directional LBP features to characterize facial fine micro-patterns are defined by LBP operation factors (direction and size of operation mask) and feature regions through AdaBoost learning. The facial expression classifier is implemented as a SVM classifier based on learned discriminant region and directional LBP operation factors. In order to verify the validity of the proposed method, facial expression recognition performance was measured in terms of accuracy, sensitivity, and specificity. Experimental results show that the proposed directional LBP and its learning method are useful for facial expression recognition.
- KSP 제안 키워드
- Directional LBP, Facial Expression Recognition(FER), Feature regions, LBP features, Learning methods, Micro-patterns, SVM Classifier, facial expression classification, recognition performance