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Automated emotion recognition in the wild from facial images remains achallenging problem. Although recent advances in Deep Learning have supposed asignificant breakthrough in this topic, strong changes in pose, orientation andpoint of view severely harm current approaches. In addition, the acquisition oflabeled datasets is costly, and current state-of-the-art deep learningalgorithms cannot model all the aforementioned difficulties. In this paper, wepropose to apply a multi-task learning loss function to share a common featurerepresentation with other related tasks. Particularly we show that emotionrecognition benefits from jointly learning a model with a detector of facialAction Units (collective muscle movements). The proposed loss functionaddresses the problem of learning multiple tasks with heterogeneously labeleddata, improving previous multi-task approaches. We validate the proposal usingtwo datasets acquired in non controlled environments, and an application topredict compound facial emotion expressions.

Multi-task, multi-label and multi-domain learning with residual convolutional networks for emotion recognition