Hand-Crafted Feature Guided Deep Learning for Facial Expression Recognition

Guohang Zeng · Jiancan Zhou · Xi Jia · Weicheng Xie · Linlin Shen

65 citations0/0 files storedInternational Conference on Automatic Face and Gesture Recognition

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Emotion and Mood Recognition Face recognition and analysis Face and Expression Recognition

Abstract

A number of facial expression recognition algorithms based on hand-crafted features and deep neutral networks have been developed. Motivated by the similarity between the hand-crafted features and features learned by deep network, a new feature loss is proposed to embed the information of hand-crafted features into the training process of network, which tries to reduce the difference between the two features. Based on the feature loss, a general framework for embedding the traditional feature information was developed and tested using CK+, JAFFE and FER2013 datasets. Experimental results show that the proposed network achieves much better accuracy than the original hand-crafted feature and the network without using our feature loss. When compared with other algorithms in literature, our network also achieved the best performance on CK+ dataset, i.e. 97.35% accuracy has been achieved.

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References

2002Comprehensive database for facial expression analysis 2016Going deeper in facial expression recognition using deep neural networks 2015Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition 2016Peak-Piloted Deep Network for Facial Expression Recognition 2015Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?2013AU-aware Deep Networks for facial expression recognition 2014Combining Multiple Kernel Methods on Riemannian Manifold for Emotion Recognition in the Wild 2016HoloNet: towards robust emotion recognition in the wild

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2021Self-Supervised Exclusive-Inclusive Interactive Learning for Multi-Label Facial Expression Recognition in the Wild 2019Adaptive Weighting of Handcrafted Feature Losses for Facial Expression Recognition 2020Fast and Efficient Facial Expression Recognition Using a Gabor Convolutional Network 2024A Deep Quantum Convolutional Neural Network Based Facial Expression Recognition For Mental Health Analysis 2019Visual Scene-aware Hybrid Neural Network Architecture for Video-based Facial Expression Recognition

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