Boosting-POOF: Boosting Part Based One vs One Feature for Facial Expression Recognition in the Wild

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, RAF-COMPOUND) 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.