Abstract
In this paper, multimodal learning for facial expression recognition (FER) is proposed. The multimodal learning method makes the first attempt to learn the joint representation by considering the texture and landmark modality of facial images, which are complementary with each other. In order to learn the representation of each modality and the correlation and interaction between different modalities, the structured regularization (SR) is employed to enforce and learn the modality-specific sparsity and density of each modality, respectively. By introducing SR, the comprehensiveness of the facial expression is fully taken into consideration, which can not only handle the subtle expression but also perform robustly to different input of facial images. With the proposed multimodal learning network, the joint representation learning from multimodal inputs will be more suitable for FER. Experimental results on the CK+ and NVIE databases demonstrate the superiority of our proposed method.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.