Enhancing Noisy Label Facial Expression Recognition With Split and Merge Consistency Regularization-Reference-Cited by-同舟云学术

Enhancing Noisy Label Facial Expression Recognition With Split and Merge Consistency Regularization

Published:2023 Issue: Volume:11 Page:140496-140505
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Kim Jihyun¹,Kwon Junehyoung¹,Kim Mihyeon¹,Lee Eunju²^ORCID,Kim Youngbin¹^ORCID

Affiliation:

1. Department of Artificial Intelligence, Chung-Ang University, Dongjak, South Korea

2. Department of Imaging Science, Multimedia and Film, Chung-Ang University, Dongjak, South Korea

Funder

Chung-Ang University Graduate Research Scholarship in 2022;

Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education

Institute for Information and Communications Technology Planning and Evaluation (IITP) through the Korea Government (MSIT) under Artificial Intelligence Graduate School Program, Chung-Ang University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10345539.pdf?arnumber=10345539

Reference44 articles.

1. The argument and evidence about universals in facial expressions of emotion;Ekman,1989

2. Facial action coding system (FACS): A technique for the measurement of facial actions;Ekman;Rivista Di Psichiatria,1978

3. Enhancement of Patient Facial Recognition through Deep Learning Algorithm: ConvNet

4. Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey

5. Emotion detection and face recognition of drivers in autonomous vehicles in IoT platform