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Pose-robust feature learning for facial expression recognition

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Abstract

Automatic facial expression recognition (FER) from non-frontal views is a challenging research topic which has recently started to attract the attention of the research community. Pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization and initialization procedures. Thus head-pose invariant facial expression recognition continues to be an issue to traditional methods. In this paper, we propose a novel approach for pose-invariant FER based on pose-robust features which are learned by deep learning methods — principal component analysis network (PCANet) and convolutional neural networks (CNN) (PRP-CNN). In the first stage, unlabeled frontal face images are used to learn features by PCANet. The features, in the second stage, are used as the target of CNN to learn a feature mapping between frontal faces and non-frontal faces. We then describe the non-frontal face images using the novel descriptions generated by the maps, and get unified descriptors for arbitrary face images. Finally, the pose-robust features are used to train a single classifier for FER instead of training multiple models for each specific pose. Our method, on the whole, does not require pose/ landmark annotation and can recognize facial expression in a wide range of orientations. Extensive experiments on two public databases show that our framework yields dramatic improvements in facial expression analysis.

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Authors and Affiliations

  1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013, China

    Feifei Zhang, Yongbin Yu, Qirong Mao, Jianping Gou & Yongzhao Zhan

Authors
  1. Feifei Zhang
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  2. Yongbin Yu
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  3. Qirong Mao
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  4. Jianping Gou
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  5. Yongzhao Zhan
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Corresponding author

Correspondence to Qirong Mao.

Additional information

Feifei Zhang received her BS degree in computer science and technology from Xuchang University, China in 2013. she is currently a MS candidate in computer technology with the School of Computer Science and Communication Engineering at Jiangsu University, China. Her research interests include affect computing and pattern recognition.

Yongbin Yu received his BS degree in computer science and technology from Jiangsu University, China in 2012. He received his MS degree of computer application technology in the School of Computer Science and Communication Engineering at Jiangsu University. His research interests include affect computing and pattern recognition.

Qirong Mao received her MS and PhD degrees from Jiangsu University, China in 2002 and 2009, both in computer application technology. She is currently an associate professor of the School of Computer Science and Communication Engineering, Jiangsu University. Her research interests include affective computing, pattern recognition, and multimedia analysis. She has published over 30 technical articles, some of them in premium journals and conferences such as ACMMultimedia, IEEE Transactions onMultimedia. She is a member of the IEEE.

Jianping Gou received the BS degree in computer science from Beifang University of Nationalities, China in 2005, the MS degree in computer science from the Southwest Jiaotong University, China in 2008, and the PhD degree in computer science from University of Electronic Science and Technology of China, China in 2012. He is currently a lecturer in School of Computer Science and Telecommunication Engineering, JiangSu University, China. His current research interests include pattern classification, machine learning.

Yongzhao Zhan received his BS degree from Fuzhou University, China in 1984 and his PhD degree from Nanjing University, China in 2000, both in computer science and technology. He is currently a professor and the dean of the School of Computer Science and Communication Engineering, Jiangsu University, China. His research interests include multimedia analysis and pattern recognition. He has published over 60 technical articles.

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Zhang, F., Yu, Y., Mao, Q. et al. Pose-robust feature learning for facial expression recognition. Front. Comput. Sci. 10, 832–844 (2016). https://doi.org/10.1007/s11704-015-5323-3

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  • DOI: https://doi.org/10.1007/s11704-015-5323-3

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