Analysis of Artificial Intelligence Based Human Expression

Facial Emotion Recognition (FER) is a current area of research in computer vision and machine learning. This study looks into how well Convolutional Neural Networks (CNNs) can identify face expressions of human emotion. CNNs have proven to perform exceptionally well in a range of computer vision app...

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Bibliographic Details
Published in2023 4th International Conference on Smart Electronics and Communication (ICOSEC) pp. 1716 - 1719
Main Authors Nikhil, Bandaru, Anitha, J., Manasa, B., Srikanth, Kolli, Kumar, K. Anil, Rajeswaran, N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.09.2023
Subjects
Classification
CNN
Computational modeling
Computer vision
Deep learning
Electric potential
Emotion recognition
Face recognition
FER
RELU
SoftMax
Training
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Summary:Facial Emotion Recognition (FER) is a current area of research in computer vision and machine learning. This study looks into how well Convolutional Neural Networks (CNNs) can identify face expressions of human emotion. CNNs have proven to perform exceptionally well in a range of computer vision applications, including image categorization, object recognition, and face recognition. To categorize the seven fundamental human emotions-anger, disgust, fear, happiness, sadness, surprise, neutral- a deep learning model is suggested utilizing CNNs. Convolutional and pooling layers are interspersed with fully connected layers for classification in the proposed model. The FER2013 dataset, which includes more than 35,000 photos, is the one utilized for training and evaluation. The performance of the proposed model was evaluated and compare it with the state-of-the-art approaches in terms of accuracy, precision, recall, and F1 score. The experimental findings show that the state-of-the-art performance in facial emotion recognition achieved by the proposed CNN-based model.
DOI:10.1109/ICOSEC58147.2023.10275960