Supervised Committee of Convolutional Neural Networks in Automated Facial Expression Analysis

Automated emotion recognition from facial images is an unsolved problem in computer vision. Although recent methods achieve close to human accuracy in controlled scenarios, the recognition of emotions in the wild remains a challenging problem. Recent advances in Deep learning have supposed a significant breakthrough in many computer vision tasks, including facial expression analysis. Particularly, the use of Deep Convolutional Neural Networks has attained the best results in the recent public challenges. The current state-of-the-art algorithms suggest that the use of ensembles of CNNs can outperform individual CNN classifiers. Two key considerations influence these results: (i) The design of CNNs involves the adjustment of parameters that allow diversity and complementarity in the partial classification results, and (ii) the final classification rule that assembles the result of the committee. In this paper we propose to improve the assembling of the committee by introducing supervised learning on the ensemble computation. We train a CNN on the posterior-class probabilities resulting from the individual members allowing to capture non-linear dependencies among committee members, and to learn this combination from data. The validation shows an accuracy 5 percent higher with respect to previous state-of-the art results based on averaging classifiers, and 4 percent to the majority voting rule.