Modified Convolutional Neural Networksfor Facial Emotion Classification

Sobia Yousaf ()
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Sobia Yousaf: Department of Software Engineering, National University of Modern Languages, Rawalpindi,Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 4, 1897-1912

Abstract: Facial expression analysis is a fascinating yet challenging problem in the realm of artificial intelligence. The vast variability in human expressions poses a significant hurdle for machine learning methods to detect them accurately. Recently, machine learning and deep learning approaches have made notable strides in this area, leveraging Deep Neural Networks (DNNs) to identify human emotions. Convolutional Neural Networks (CNNs), in particular, have proven effective in resolving the complexities involved inhuman facial expressions, making them a preferred choice for these tasks.In this study, we proposeda modified CNN architecture by introducing a new layer to enhance accuracy. The CNN network is trained on both frontal face images and images with varying poses. We utilized three distinct datasetsFER 2013, CK+and our own dataset to achieve the desired results. The evaluation results obtained using the proposed network surpass those achieved by conventional CNN networks. Notably, our proposed network achieves an average accuracy of 97.5% on our collected dataset.

Keywords: Facial Expression Recognition; Facial Action Coding System; Machine Learning; Convolutional Neural Networks; Artificial Intelligence; Deep Learning; Extended Cohn-Kanade(CK+) (search for similar items in EconPapers)
Date: 2024
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