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A Lightweight Facial Emotion Recognition System Using Partial Transfer Learning for Visually Impaired People

Shehada, D, Turky, A, Khan, W, Khan, B and Hussain, A (2023) A Lightweight Facial Emotion Recognition System Using Partial Transfer Learning for Visually Impaired People. IEEE Access, 11. pp. 36961-36969.

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The inability to perceive visual and other non-verbal cues for individuals with visual impairment can pose a significant challenge for their correct conversational interactions and can be an impediment for various daily life activities. Recent advancements in computational resources, particularly the computer vision capabilities can be utilized to design effective applications for visually impaired people (VIP).Among various assistive technologies, automated facial impression recognition with real-time accurate interpretation can be proven useful to tackle the above problem. Using such approach, facial emotions (e.g., sad, happy) can be robustly recognized and conveyed to the associated individuals. In this paper, a partial transfer learning approach is adopted utilizing a custom trained Convolutional Neural Network (CNN) for facial emotion recognition. A novel model that transfers features from one dataset to another is proposed. This model enables the transfer of features learned from a small number of instances to solve new challenging instances. Using the proposed approach based on a newly trained CNN, a portable lightweight facial expression recognition system with wireless connectivity and high detection accuracy was constructed and targeted specifically for VIP. The proposed recognition model provides a notable improvement over the current state-of-the-art, by providing the highest recognition accuracy of 82.1% on the enhanced Facial Expression Recognition 2013 (FER2013) dataset. Moreover, with only 1.49M parameters, the model is operable on edge devices with limited memory and processing power. Overall, three labeled emotions happy, sad, surprise were recognized by the model with high accuracy whereas a relatively lower accuracy rate for anger, disgust, fear was noticed with higher misclassification labels for sad.

Item Type: Article
Uncontrolled Keywords: 08 Information and Computing Sciences; 09 Engineering; 10 Technology
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
H Social Sciences > HV Social pathology. Social and public welfare. Criminology > HV697 Protection, assistance and relief > HV1551 People with disabilities
Divisions: Computer Science & Mathematics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
SWORD Depositor: A Symplectic
Date Deposited: 03 May 2023 11:24
Last Modified: 03 May 2023 11:30
DOI or ID number: 10.1109/access.2023.3264268
URI: https://researchonline.ljmu.ac.uk/id/eprint/19455
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