2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLR-APPL-IP-6.8
Paper Title LEARNING ASSOCIATIVE REPRESENTATION FOR FACIAL EXPRESSION RECOGNITION
Authors Yangtao Du, Dingkang Yang, Peng Zhai, Mingchen Li, Lihua Zhang, Institute of AI and Robotics, Fudan University, China
SessionMLR-APPL-IP-6: Machine learning for image processing 6
LocationArea E
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract The main inherent challenges with the Facial Expression Recognition (FER) are high intra-class variations and high inter-class similarities, while existing methods pay little attention to the association within inter- and intra-class expressions. This paper introduces a novel Expression Associative Network (EAN) to learn association of facial expression, specifically, from two aspects: 1) associative topological relation over mini-batch is constructed by similarity matrix with an adjacent regularization, and 2) learning association of expressions with Graph Convolutional Network (GCN). Besides, an auxiliary module as invariant feature generator based on Generative Adversarial Networks (GAN) is designed to suppress pose variations, illumination changes, and occlusions. Results on public benchmarks achieve comparable or better performance compared with current state-of-the-art methods, with 90.07% on FERPlus, 86.36% on RAF-DB, and improve by 3.92% over SOTA on synthetic wrong labeling datasets.