Abstract
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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Araujo, R., Miao, YQ., Kamel, M.S., Cheriet, M. (2012). A Fast and Robust Feature Set for Cross Individual Facial Expression Recognition. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2012. Lecture Notes in Computer Science, vol 7594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33564-8_33
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DOI: https://doi.org/10.1007/978-3-642-33564-8_33
Publisher Name: Springer, Berlin, Heidelberg
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