1. Two-Stage Recognition and beyond for Compound Facial Emotion Recognition
- Author
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Kamal Nasrollahi, Davit Rizhinashvili, Sergio Escalera, Thomas B. Moeslund, Kadir Aktas, Abdallah Hussein Sham, Gholamreza Anbarjafari, Dorota Kamińska, and Danila Kuklyanov
- Subjects
Facial expression ,TK7800-8360 ,Computer Networks and Communications ,media_common.quotation_subject ,Speech recognition ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Cultural diversity ,ComputerApplications_MISCELLANEOUS ,Aprenentatge automàtic ,Machine learning ,facial expression recognition ,Reconeixement de formes (Informàtica) ,Emotion recognition ,Compound emotion recognition ,Electrical and Electronic Engineering ,compound emotion recognition ,media_common ,dominant and complementary emotion recognition ,business.industry ,Deep learning ,Visió per ordinador ,Gender distribution ,Expressió facial ,deep learning ,Pattern recognition systems ,ComputingMethodologies_PATTERNRECOGNITION ,Facial expression recognition ,Hardware and Architecture ,Control and Systems Engineering ,Signal Processing ,Dominant and complementary emotion recognition ,Computer vision ,Artificial intelligence ,Electronics ,business ,Psychology ,Diversity (politics) - Abstract
Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition, 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels.
- Published
- 2021
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