Self-cure network with two-stage method for facial expression recognition

Yifan Chen, Dongsheng Wu*, Jiahui Yu, Zhaojie Ju, Qing Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

It is very difficult to annotate large-scale facial expressions due to the inconsistent labels caused by the subjectivity of the annotators and the ambiguity of the facial expressions. Moreover, current studies present limitation when addressing facial expression different due to gender gap. Therefore, this artical proposes a self-cure network with two-stage method(SCN-TSM) which prevents deep networks from over-fitting ambiguous images. First, base on SCN-TSM, a two-stage training scheme is designed, taking full advantage of the gendered information. Furthermore, a self-attention mechanism to highlight the essential images, and to weight each sample with a weighting regularization. Finally, a relabeling module to modify the labels of these samples in inconsistent labels. A large number of experiments on public datasets validate the effectiveness of our method.

Original languageEnglish
Title of host publication2021 27th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2021
PublisherInstitute of Electrical and Electronics Engineers
Pages163-168
Number of pages6
ISBN (Electronic)9781665431538
ISBN (Print)9781665431545
DOIs
Publication statusPublished - 7 Jan 2022
Event2021 27th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2021 - Shanghai, China
Duration: 26 Nov 202128 Nov 2021

Conference

Conference2021 27th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2021
Country/TerritoryChina
CityShanghai
Period26/11/2128/11/21

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