A Transfer Learning Approach for Facial Emotion Recognition Using a Deep Learning Model

Nahia Nowreen Urnisha, Sanjida Islam Bithi, Md. Mushtaq Shahriyar Rafee, Nasif Istiak Remon, Md. Mahfujul Hasan and Rajarshi Roy Chowdhury
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Nahia Nowreen Urnisha: Department of Computer science and Engineering, Metropolitan University, Sylhet, Bangladesh
Sanjida Islam Bithi: Department of Computer science and Engineering, Metropolitan University, Sylhet, Bangladesh
Md. Mushtaq Shahriyar Rafee: Department of Computer science and Engineering, Metropolitan University, Sylhet, Bangladesh
Nasif Istiak Remon: Department of Computer science and Engineering, Metropolitan University, Sylhet, Bangladesh
Md. Mahfujul Hasan: Department of Computer science and Engineering, Metropolitan University, Sylhet, Bangladesh
Rajarshi Roy Chowdhury: Department of Computer science, American International University-Bangladesh, Dhaka, Bangladesh

International Journal of Research and Scientific Innovation, 2024, vol. 11, issue 4, 274-284

Abstract: The facial expression recognition (FER) system is the process of identifying the emotional state of a person. Emotion recognition from facial expressions is a rapidly growing area of research with numerous applications, including psychology, marketing, and human-computer interaction. This paper presents a novel approach to FER employing deep learning techniques, specifically leveraging the power of transfer learning methodology. To address the challenge of accurately recognizing different emotions, including angry, disgusted, afraid, happy, sad, surprised, and neutral, from facial expressions, a pre-trained model MobileNetV2 architecture has been fine-tuned for improving accuracy. It is a lightweight convolutional neural network architecture, specifically designed for efficient on-device inference. For evaluating the performance of the proposed FER model, the state-of-the-art FER-2013 dataset, along random images and video clips has been employed. Experimental results demonstrate that the proposed FER model attained a remarkable accuracy rate exceeding 99% when tested on diverse sets of random images and video clips. Moreover, the system achieved a notable accuracy of 61% when evaluated against the FER-2013 dataset. Overall, this approach presents a significant advancement in the field of real-time facial expression recognition using the MobileNetV2 architecture with FER2013 dataset, which may improve the quality of human-computer interactions.

Date: 2024
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