Learning Features Robust to Image Variations with Siamese Networks for Facial Expression Recognition

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• 关键词：Deep learning ; Convolutional neural networks ; Siamese ; network ; Facial expression recognition
• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10132
• 期：1
• 页码：189-200
• 丛书名：MultiMedia Modeling
• ISBN：978-3-319-51811-4
• 卷排序：10132

文摘

This paper proposes a computationally efficient method for learning features robust to image variations for facial expression recognition (FER). The proposed method minimizes the feature difference between an image under a variable image variation and a corresponding target image with the best image conditions for FER (i.e. frontal face image with uniform illumination). This is achieved by regulating the objective function during the learning process where a Siamese network is employed. At the test stage, the learned network parameters are transferred to a convolutional neural network (CNN) with which the features robust to image variations can be obtained. Experiments have been conducted on the Multi-PIE dataset to evaluate the proposed method under a large number of variations including pose and illumination. The results show that the proposed method improves the FER performance under different variations without requiring extra computational complexity.