The IntraFace (IF) is a library with state of the art algorithms for feature tracking, head pose estimation, facial attribute recognition, multi-person face detection and analysis, facial expression recognition, and facial synchrony detection.
IF implements facial feature detection and tracking using the Supervised Descent Method (SDM). The facial attributes are classified using a linear SVM with HoG features. The Selective Transfer Machine (STM) is adopted to compensate face variations, such as pose, scale, illumination, occlusion, and individual differences in facial morphology and behavior.
The library provides algorithms to perform basic emotion recognition and facial Action Units (AU) detection. To consolidate the results, IF algorithms were compared against generic and state of the art ones, such as linear SVM, Kernel Mean Matching (KMM), Transductive SVM ( T-SVM), and Domain Adaptation SVM (DA-SVM).
It was used the CK+ and the FERA datasets. Both datasets contain videos where subjects perform one emotional state, however FERA is a more challenging dataset, i.e. it is harder to segment an emotional state from the neutral expression.
In the average IF performed better in both datasets in the tasks of emotion recognition and AU detection. In the CK+ dataset, SVM performed close to IF due to the separability of the emotions from the neutral face. In the FERA, the DA-SVM performed closest to IF, as the SVM adapt to the emotion.
In the author’s website it is provided the IntraFace C++ library, however its usage is free of charge only for academics purposes. The source code is closed and their algorithms are patent pending.
“IntraFace”, F. De La Torre, W. Chu, X. Xiong, F. Vicente, J. F. Cohn – IEEE International Conference on Face and Gesture Recognition , 2015.