| Summary: | 碩士 === 國立臺灣科技大學 === 電機工程系 === 107 === Facial expression recognition (FER) based on facial features is an important and challenging problem for automatic inspection of surveillance videos. In this thesis, a hybrid facial expression recognition system is proposed based on facial features, and a total of six facial expressions can be recognized. For the pre-processing, the Random Forest classifier is applied to track the facial landmark points, which is utilized for face alignment. In the feature extraction, with the advance of deep learning technology, we introduced the hybrid RNN technique by incorporating with deep learning to extract robust features. In addition, the geometrical features and the facial action unit based on the movement of the facial feature point are also considered. As the results, we evaluate the proposed method on the two database, CK+ and Oulu-CASIA, and compare with previous works. Though there are uncontrolled factors in the videos, such as lighting and head posture, the proposed method can achieve superior performance than former schemes. Thus, the proposed method has considerable potential to be applied in the practical applications.
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