Deep Learning Models for Facial Expression Recognition

Atul Sajjanhar, Zhaoqi Wu, Q. Wen
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引用次数: 25

Abstract

We investigate facial expression recognition using state-of-the-art classification models. Recently, CNNs have been extensively used for face recognition. However, CNNs have not been thoroughly evaluated for facial expression recognition. In this paper, we train and test a CNN model for facial expression recognition. The performance of the CNN model is used as benchmark for evaluating other pre-trained deep CNN models. We evaluate the performance of Inception and VGG which are pre-trained for object recognition, and compare these with VGG-Face which is pre-trained for face recognition. All experiments are performed on publicly available face databases, namely, CK+, JAFFE and FACES.
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面部表情识别的深度学习模型
我们使用最先进的分类模型研究面部表情识别。近年来,cnn被广泛应用于人脸识别。然而,cnn在面部表情识别方面还没有得到彻底的评估。在本文中,我们训练和测试了一个用于面部表情识别的CNN模型。该CNN模型的性能被用作评价其他预训练深度CNN模型的基准。我们评估了Inception和VGG的性能,并将其与人脸识别的VGG- face进行了比较。所有实验均在公开的人脸数据库上进行,即CK+、JAFFE和FACES。
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