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2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) (2017)
Washington, DC, DC, USA
May 30, 2017 to June 3, 2017
ISBN: 978-1-5090-4023-0
pp: 967-972
Recent years, facial expression recognition has remained a challenging and interesting problem, especially for faces in the real world. Most of the traditional approaches are based on Action Units (AUs) detection or low-level features (e.g. LBP, HOG, SIFT and Gabor). Thus, when recognizing real-world facial expressions, these methods might result in poor performance. In this paper, we propose an automatic framework called ‘Boosting-POOF’ to extract discriminative Mid-Level features using low-level features extracted from local face regions. Rather than cascade local features altogether, we adopt class-pairwise Mid-Level descriptors for each local region to extract Mid-Level features and Adaboost feature selection to choose more discriminative features. In experiments, four facial expression benchmarks (CK+, SFEW, RAF-BASIC, RAFCOMPOUND) are evaluated. The ‘Boosting-POOF’ achieves state-of-the-art performance compared with recent approaches. What’ more, the ‘Boosting-POOF’ can automatically provide the most significant difference between two expression categories, which is more useful than AUs detection for real world images.

Z. Liu, S. Li and W. Deng, "Boosting-POOF: Boosting Part Based One vs One Feature for Facial Expression Recognition in the Wild," 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)(FG), Washington, DC, DC, USA, 2017, pp. 967-972.
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