A Multi-Class Facial Emotion Recognition System for Still Images 1Omage Micheal, 2Fasola Olusanjo & 3Woods Nancy C. 1,3University of Ibadan, Ibadan, Nigeria 2Federal University of Technology, Minna, Nigeria E-mails: 1micheal.omage@gmail.com, 2Sanjo.fasola@futminna.edu.ng, 3Chyn.woods@gmail.com Phone Nos: 1+2348084121913, 2+2348023138034, 3+2348037273291 ABSTRACT Facial expressions serve as a potent and natural means of human communication, conveying a wide range of emotions. In the realm of artificial intelligence and computer vision, recognizing these emotions from facial cues has gained significant importance. Most research focus on detecting a single dominant emotion on any given facial image, although an image could portray several emotions. This work presents a multi-class facial emotion recognition system capable of predicting and quantifying the presence of multiple emotions in a single facial image. The system employs a Convolutional Neural Network (CNN) architecture optimized for multi-label classification across eight emotion classes. The AffectNet dataset, comprising 27,836 images with diverse ages, ethnicities, and real-world settings, was utilized for training the model, to enhance generalization capabilities. The model's performance was evaluated on an impartial benchmark dataset, FER-Plus, containing 35,710 images. The CNN model performed well, with a micro F1-score of 0.7 and a macro F1-score of 0.7 on the test set from AffectNet, and a micro F1-score of 0.7 and a macro F1-score of 0.4 on the FER-Plus dataset. Qualitative analysis demonstrated the model's capability in recognizing blended and subtle facial expressions, providing significant probability distributions across multiple emotion labels. The developed system advances the state-of-the-art in multi-label facial emotion recognition by successfully modeling emotion mixtures, contributing to a deeper understanding of this challenging task and enabling practical applications in affective computing, human-computer interaction, psychological studies, and improving accessibility and inclusivity in emotional computing technologies. Keywords: Multi-Class, Facial Emotion Recognition, Security, System, Still Images, Models, Detection Omage, M. Fasola, O. & Woods, N.C. (2024): A Multi-Class Facial Emotion Recognition System for Still Images. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 3. Pp 67-80. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N3P6
Authors:   1,3University of Ibadan, Ibadan, Nigeria 2Federal University of Technology, Minna, Nigeria, Omage Micheal , Fasola Olusanjo , Woods Nancy C 
Published: November 2024

Facial expressions serve as a potent and natural means of human communication, conveying a wide range of emotions. In the realm of artificial intelligence and computer vision, recognizing these emotions from facial cues has gained significant importance. Most research focus on detecting a single dominant emotion on any given facial image, although an image could portray several emotions. This work presents a multi-class facial emotion recognition system capable of predicting and quantifying the presence of multiple emotions in a single facial image. The system employs a Convolutional Neural Network (CNN) architecture optimized for multi-label classification across eight emotion classes. The AffectNet dataset, comprising 27,836 images with diverse ages, ethnicities, and real-world settings, was utilized for training the model, to enhance generalization capabilities. The model's performance was evaluated on an impartial benchmark dataset, FER-Plus, containing 35,710 images. The CNN model performed well, with a micro F1-score of 0.7 and a macro F1-score of 0.7 on the test set from AffectNet, and a micro F1-score of 0.7 and a macro F1-score of 0.4 on the FER-Plus dataset. Qualitative analysis demonstrated the model's capability in recognizing blended and subtle facial expressions, providing significant probability distributions across multiple emotion labels. The developed system advances the state-of-the-art in multi-label facial emotion recognition by successfully modeling emotion mixtures, contributing to a deeper understanding of this challenging task and enabling practical applications in affective computing, human-computer interaction, psychological studies, and improving accessibility and inclusivity in emotional computing technologies. Keywords: Multi-Class, Facial Emotion Recognition, Security, System, Still Images, Models, Detection Omage, M. Fasola, O. & Woods, N.C. (2024): A Multi-Class Facial Emotion Recognition System for Still Images. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 3. Pp 67-80. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N3P6