Handling missing labels and class imbalance challenges simultaneously for facial action unit recognition

Abstract

Facial action unit (AU) recognition has attracted great attention because of the applications in a wide range of fields. Missing labels and class imbalance (CIB) are both challenges for facial action unit recognition. Missing labels means that there are only apart label assignments for training samples. CIB is observed from two perspectives: firstly, the number of positive AUs is much smaller than that of negative AUs for each expressional image; secondly, the rate of positive samples of different AUs are significantly different. Both missing labels and CIB lead to performance degradation in AU recognition. In this work, we propose to handle these two challenges in AU recognition simultaneously. Specifically, we formulate AU recognition with missing labels as a multi label learning with missing labels (MLML) problem, which handles the missing label challenge naturally. However, different from most existing MLML approaches which usually employ same features from whole image for all classes, we select the most related features for each AU. To handle the CIB challenge, we further introduce class cardinality bounds which constrain the number of positive AUs for each data instance, as well as the number of positive labels for each AU in the overall dataset. The class cardinality bounds serve as linear constraints for the objective function, which turns the optimization NP-hard. Thus we present convex approximation based on the Lovasz extension, which leads to a linear program that can be efficiently solved by the alternative direction method of multipliers (ADMM). Experimental results on both posed and spontaneous facial expression datasets demonstrate the superiority of the proposed method compared to state-of-the-art.