Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction

• URL: http://arxiv.org/abs/2206.09509v1
• Date: Sun, 19 Jun 2022 23:43:41 GMT
• Title: Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction
• Authors: Ozioma Collins Oguine (1), Kaleab Alamayehu Kinfu (2), Kanyifeechukwu Jane Oguine (1), Hashim Ibrahim Bisallah (1), Daniel Ofuani (1) ((1) Department of Computer Science, University of Abuja, Nigeria, (2) Department of Computer Science, Johns Hopkins University, Baltimore, USA)
• Abstract summary: This paper proposes a hybrid model for Facial Expression recognition, which comprises a Deep Convolutional Neural Network (DCNN) and Haar Cascade deep learning architectures. The objective is to classify real-time and digital facial images into one of the seven facial emotion categories considered. The experimental results show a significantly improved classification performance compared to state-of-the-art experiments and research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Facial Expression Recognition is a vital research topic in most fields ranging from artificial intelligence and gaming to Human-Computer Interaction (HCI) and Psychology. This paper proposes a hybrid model for Facial Expression recognition, which comprises a Deep Convolutional Neural Network (DCNN) and Haar Cascade deep learning architectures. The objective is to classify real-time and digital facial images into one of the seven facial emotion categories considered. The DCNN employed in this research has more convolutional layers, ReLU Activation functions, and multiple kernels to enhance filtering depth and facial feature extraction. In addition, a haar cascade model was also mutually used to detect facial features in real-time images and video frames. Grayscale images from the Kaggle repository (FER-2013) and then exploited Graphics Processing Unit (GPU) computation to expedite the training and validation process. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. The experimental results show a significantly improved classification performance compared to state-of-the-art (SoTA) experiments and research. Also, compared to other conventional models, this paper validates that the proposed architecture is superior in classification performance with an improvement of up to 6%, totaling up to 70% accuracy, and with less execution time of 2098.8s.

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