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A discriminative deep association learning for facial expression recognition

  • Xing Jin
  • Wenyun Sun
  • Zhong JinEmail author
Original Article

Abstract

Deep learning based facial expression recognition becomes more successful in many applications. However, the lack of labeled data is still a bottleneck for better recognition performance. Thus, it is of practical significance to exploit the rich unlabeled data for training deep neural networks (DNNs). In this paper, we propose a novel discriminative deep association learning (DDAL) framework. The unlabeled data is provided to train the DNNs with the labeled data simultaneously, in a multi-loss deep network based on association learning. Moreover, the discrimination loss is also utilized to ensure intra-class clustering and inter-class centers separating. Furthermore, a large synthetic facial expression dataset is generated and used as unlabeled data. By exploiting association learning mechanism on two facial expression datasets, competitive results are obtained. By utilizing synthetic data, the performance is increased clearly.

Keywords

Facial expression recognition Association learning Deep network Synthetic facial expression 

Notes

Acknowledgements

This work is partially supported by National Natural Science Foundation of China under Grant Nos. 61872188, U1713208, 61602244, 61672287, 61702262, 61773215. Meanwhile, this work is partially supported by China Postdoctoral Science Foundation under Grant No.2018M643183.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and Engineering and Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of EducationNanjing University of Science and TechnologyNanjingChina
  2. 2.College of Electronics and Information EngineeringShenzhen UniversityShenzhenChina

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