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Current state-of-the-art models for automatic Facial Expression Recognition(FER) are based on very deep neural networks that are effective but ratherexpensive to train. Given the dynamic conditions of FER, this characteristichinders such models of been used as a general affect recognition. In thispaper, we address this problem by formalizing the FaceChannel, a light-weightneural network that has much fewer parameters than common deep neural networks.We introduce an inhibitory layer that helps to shape the learning of facialfeatures in the last layer of the network and thus improving performance whilereducing the number of trainable parameters. To evaluate our model, we performa series of experiments on different benchmark datasets and demonstrate how theFaceChannel achieves a comparable, if not better, performance to the currentstate-of-the-art in FER. Our experiments include cross-dataset analysis, toestimate how our model behaves on different affective recognition conditions.We conclude our paper with an analysis of how FaceChannel learns and adapt thelearned facial features towards the different datasets.

The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition