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Learning Framework for Real-World Facial Emotion Recognition

Learning Framework for Real-World Facial Emotion Recognition

Rohan Appasaheb Borgalli, Sunil Surve
Copyright: © 2023 |Pages: 32
ISBN13: 9781668481714|ISBN10: 1668481715|ISBN13 Softcover: 9781668481721|EISBN13: 9781668481738
DOI: 10.4018/978-1-6684-8171-4.ch003
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MLA

Borgalli, Rohan Appasaheb, and Sunil Surve. "Learning Framework for Real-World Facial Emotion Recognition." AI-Enabled Social Robotics in Human Care Services, edited by Sandeep Kautish, et al., IGI Global, 2023, pp. 59-90. https://doi.org/10.4018/978-1-6684-8171-4.ch003

APA

Borgalli, R. A. & Surve, S. (2023). Learning Framework for Real-World Facial Emotion Recognition. In S. Kautish, N. Chaubey, S. Goyal, & P. Whig (Eds.), AI-Enabled Social Robotics in Human Care Services (pp. 59-90). IGI Global. https://doi.org/10.4018/978-1-6684-8171-4.ch003

Chicago

Borgalli, Rohan Appasaheb, and Sunil Surve. "Learning Framework for Real-World Facial Emotion Recognition." In AI-Enabled Social Robotics in Human Care Services, edited by Sandeep Kautish, et al., 59-90. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-8171-4.ch003

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Abstract

Facial expression recognition (FER) is an important research area in the fields of computer vision and artificial intelligence due to its application in academics as well as in industry. Research shows that using facial images/videos for recognition of facial expression is better because visual expressions carry major information through which emotions can be conveyed. Past research on FER has focused on the study of seven basic emotions; however, many more facial expressions are exhibited by humans that are considered compound emotions. State of art results shows machine learning and deep learning-based approaches are powerful over conventional FER approaches. This chapter focuses on surveying past work done in the field of real-world compound facial emotion recognition and implementing various learning frameworks such as machine learning and deep learning for real-world facial emotion recognition systems for detecting compound emotion using the facial expression image dataset RAF-DB for a real wild scenario.

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