A Fast and Robust Feature Set for Cross Individual Facial Expression Recognition
This paper presents a new simple and robust set of features to classify emotional states in sequences of facial images. The proposed method is derived from simple geometric-based features that deliver a fast, highly discriminative, low-dimensional, and robust classification across individuals. The proposed method was compared to other state-of-the-art methods such as Gabor, LBP and AAM-based features. They were all compared using four different classifiers and experimental results based on these classifiers have shown that the proposed features are more stable in “leave-same-sequence-image-out” (LSSIO) environments, less computational intense and faster when compared to others.
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