Automatic Multiface Expression Recognition Using Convolutional Neural Network

Padmapriya K.C., Leelavathy V., Angelin Gladston

Source Title: International Journal of Artificial Intelligence and Machine Learning (IJAIML)11(2)

ISSN: 2642-1577|EISSN: 2642-1585|EISBN13: 9781799864110|DOI: 10.4018/IJAIML.20210701.oa8

MLA

Padmapriya K.C., et al. "Automatic Multiface Expression Recognition Using Convolutional Neural Network." IJAIML vol.11, no.2 2021: pp.1-13. http://doi.org/10.4018/IJAIML.20210701.oa8

APA

Padmapriya K.C., Leelavathy V., & Gladston, A. (2021). Automatic Multiface Expression Recognition Using Convolutional Neural Network. International Journal of Artificial Intelligence and Machine Learning (IJAIML), 11(2), 1-13. http://doi.org/10.4018/IJAIML.20210701.oa8

Chicago

Padmapriya K.C., Leelavathy V., and Angelin Gladston. "Automatic Multiface Expression Recognition Using Convolutional Neural Network," International Journal of Artificial Intelligence and Machine Learning (IJAIML) 11, no.2: 1-13. http://doi.org/10.4018/IJAIML.20210701.oa8

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Abstract

The human facial expressions convey a lot of information visually. Facial expression recognition plays a crucial role in the area of human-machine interaction. Automatic facial expression recognition system has many applications in human behavior understanding, detection of mental disorders and synthetic human expressions. Recognition of facial expression by computer with high recognition rate is still a challenging task. Most of the methods utilized in the literature for the automatic facial expression recognition systems are based on geometry and appearance. Facial expression recognition is usually performed in four stages consisting of pre-processing, face detection, feature extraction, and expression classification. In this paper we applied various deep learning methods to classify the seven key human emotions: anger, disgust, fear, happiness, sadness, surprise and neutrality. The facial expression recognition system developed is experimentally evaluated with FER dataset and has resulted with good accuracy.