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The FaceChannel: A Light-weight Deep Neural Network for Facial Expression Recognition
Current state-of-the-art models for automatic FER are based on very deep...
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The FaceChannelS: Strike of the Sequences for the AffWild 2 Challenge
Predicting affective information from human faces became a popular task ...
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Optimal Architecture for Deep Neural Networks with Heterogeneous Sensitivity
This work presents a neural network that consists of nodes with heteroge...
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Noisy Student Training using Body Language Dataset Improves Facial Expression Recognition
Facial expression recognition from videos in the wild is a challenging t...
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A Sub-Layered Hierarchical Pyramidal Neural Architecture for Facial Expression Recognition
In domains where computational resources and labeled data are limited, s...
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Deep Evolution for Facial Emotion Recognition
Deep facial expression recognition faces two challenges that both stem f...
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Fast and Effective Adaptation of Facial Action Unit Detection Deep Model
Detecting facial action units (AU) is one of the fundamental steps in au...
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The FaceChannel: A Fast Furious Deep Neural Network for Facial Expression Recognition
Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. Given the dynamic conditions of FER, this characteristic hinders such models of been used as a general affect recognition. In this paper, we address this problem by formalizing the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We introduce an inhibitory layer that helps to shape the learning of facial features in the last layer of the network and thus improving performance while reducing the number of trainable parameters. To evaluate our model, we perform a series of experiments on different benchmark datasets and demonstrate how the FaceChannel achieves a comparable, if not better, performance to the current state-of-the-art in FER. Our experiments include cross-dataset analysis, to estimate how our model behaves on different affective recognition conditions. We conclude our paper with an analysis of how FaceChannel learns and adapt the learned facial features towards the different datasets.
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