FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition

Hui Ding · S. Kevin Zhou · Rama Chellappa

416 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

Relatively small data sets available for expression recognition research make the training of deep networks very challenging. Although fine-tuning can partially alleviate the issue, the performance is still below acceptable levels as the deep features probably contain redundant information from the pretrained domain. In this paper, we present FaceNet2ExpNet, a novel idea to train an expression recognition network based on static images. We first propose a new distribution function to model the high-level neurons of the expression network. Based on this, a two-stage training algorithm is carefully designed. In the pre-training stage, we train the convolutional layers of the expression net, regularized by the face net; In the refining stage, we append fully-connected layers to the pre-trained convolutional layers and train the whole network jointly. Visualization results show that the model trained with our method captures improved high-level expression semantics. Evaluations on four public expression databases, CK+, Oulu- CASIA, TFD, and SFEW demonstrate that our method achieves better results than state-of-the-art.

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References

2010The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression 2016Going deeper in facial expression recognition using deep neural networks 2015Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning 2005Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior 2015Image based Static Facial Expression Recognition with Multiple Deep Network Learning

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2021Learning Deep Global Multi-Scale and Local Attention Features for Facial Expression Recognition in the Wild 2021Facial Emotion Recognition Using Transfer Learning in the Deep CNN 2019Local Learning With Deep and Handcrafted Features for Facial Expression Recognition 2019Deep Learning for Human Affect Recognition: Insights and New Developments 2021Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition 2019A snapshot research and implementation of multimodal information fusion for data-driven emotion recognition 2018Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System

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