Visual learning of texture descriptors for facial expression recognition in thermal imagery
Computer Vision and Image Understanding
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
Facial expression recognition of a speaker using vowel judgment and thermal image processing
Artificial Life and Robotics
Image Processing: Methods, Applications and Challenges
Image Processing: Methods, Applications and Challenges
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We previously developed a method for the facial expression recognition of a speaker. For facial expression recognition, we selected three static images at the timing positions of just before speaking and while speaking the phonemes of the first and last vowels. Then, only the static image of the front-view face was used for facial expression recognition. However, frequent updates of the training data were time-consuming. To reduce the time for updates, we found that the classifications of "neutral", "happy", and "others" were efficient and accurate for facial expression recognition. Using the proposed method with updated training data of "happy" and "neutral" after an interval such as approximately three and a half years, the facial expressions of two subjects were discriminable with 87.0 % accuracy for the facial expressions of "happy", "neutral", and "others" when exhibiting the intentional facial expressions of "angry", "happy", "neutral", "sad", and "surprised".