Seongsoo Lee
Seongsoo Lee has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Min-Cheol Hong
School of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea
1 shared publication - Minh-Thien Duong
Department of Automatic Control, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City 70000, Vietnam
1 shared publication - Quoc-Thien Ho1 shared publication
- Trong-Dong Pham
Department of Information and Telecommunication Engineering, Soongsil University, Seoul 06978, Republic of Korea
1 shared publication
Latest Publications
- Open Access
- 1 November 2023
- Published by MDPI AG in Preprints.org
CNN-Based Facial Expression Recognition with Simultaneous Consideration of Inter-class and Intra-class Variations
Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones.
- 1 November 2023
- Published by MDPI AG in Preprints.org
Open AccessCNN-Based Facial Expression Recognition with Simultaneous Consideration of Inter-class and Intra-class Variations
Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones.