안정적인 실시간 얼굴 특징점 추적과 감정인식 응용
Robust Real-time Tracking of Facial Features with Application to Emotion Recognition
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초록
Facial feature extraction and tracking are essential steps in human-robot-interaction (HRI)field such as face recognition, gaze estimation, and emotion recognition. Active shape model (ASM)is one of the successful generative models that extract the facial features. However, applying only ASM is not adequate for modeling a face in actual applications, because positions of facial features are unstably extracted due to limitation of the number of iterations in the ASM fitting algorithm. The unaccurate positions of facial features decrease the performance of the emotion recognition. In this paper, we propose real-time facial feature extraction and tracking framework using ASM and LK optical flow for emotion recognition. LK optical flow is desirable to estimate time-varying geometric parameters in sequential face images. In addition, we introduce a straightforward method to avoid tracking failure caused by partial occlusions that can be a serious problem for tracking based algorithm. Emotion recognition experiments with k-NN and SVM classifier shows over 95%classification accuracy for three emotions: "joy", "anger", and "disgust".
Abstract
Facial feature extraction and tracking are essential steps in human-robot-interaction (HRI)field such as face recognition, gaze estimation, and emotion recognition. Active shape model (ASM)is one of the successful generative models that extract the facial features. However, applying only ASM is not adequate for modeling a face in actual applications, because positions of facial features are unstably extracted due to limitation of the number of iterations in the ASM fitting algorithm. The unaccurate positions of facial features decrease the performance of the emotion recognition. In this paper, we propose real-time facial feature extraction and tracking framework using ASM and LK optical flow for emotion recognition. LK optical flow is desirable to estimate time-varying geometric parameters in sequential face images. In addition, we introduce a straightforward method to avoid tracking failure caused by partial occlusions that can be a serious problem for tracking based algorithm. Emotion recognition experiments with k-NN and SVM classifier shows over 95%classification accuracy for three emotions: "joy", "anger", and "disgust".
참고문헌 (12)
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