Abstract
Aptitude, in terms of human facial recognition, cases prior one of digital image’s fundamental parts. This conveys facial parameters in many social contexts. Medical imaging, robotics, intrusion detection system with sentiment analysis, and automation and some industries use computer vision to understand human facial expressions. Studying human facial expressions using deep learning has become popular in recent years, and several efforts have been made. However, facial expression recognition remains challenging because of the wide range of persons with similar facial expressions. This paper proposed a 16-layer efficient CNN technique to categorize human facial expressions with data augmentation. Then, we evaluated our proposed approach on a well-known facial expression recognition, the FER2013 benchmark dataset. And, the proposed technique achieves state-of-the-art testing accuracy of 89.89\(\%\) exceeding some prior research.
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Refat, M.A.R., Sarker, S., Kaushal, C., Kaur, A., Islam, M.K. (2023). WhyMyFace: A Novel Approach to Recognize Facial Expressions Using CNN and Data Augmentations. In: Dutta, P., Bhattacharya, A., Dutta, S., Lai, WC. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1348. Springer, Singapore. https://doi.org/10.1007/978-981-19-4676-9_48
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