Journal article 5 views
Hybrid Reinforcement Learning With Optimized SARSA for Improved Face Recognition Systems
Anil Kumar Yadav 
,
Purushottam Sharma ,
Cheng Cheng ,
Nirmal Kumar Gupta
,
Purushottam Sharma ,
Cheng Cheng ,
Nirmal Kumar Gupta
Journal of Electrical and Computer Engineering, Volume: 2025, Issue: 1
Swansea University Author:
Cheng Cheng
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1155/jece/3305430
Abstract
Face recognition is a key technique in modern image processing, yet it faces challenges such as achieving high accuracy, reducing computational time, and optimizing memory usage. This research proposes a hybrid model that integrates an enhanced State-Action-Reward-State-Action (SARSA) reinforcement...
| Published in: | Journal of Electrical and Computer Engineering |
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| ISSN: | 2090-0147 2090-0155 |
| Published: |
Wiley
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa70224 |
| Abstract: |
Face recognition is a key technique in modern image processing, yet it faces challenges such as achieving high accuracy, reducing computational time, and optimizing memory usage. This research proposes a hybrid model that integrates an enhanced State-Action-Reward-State-Action (SARSA) reinforcement learning (RL) framework to address these challenges in face recognition tasks. The model utilizes principal component analysis (PCA) for dimensionality reduction and initial feature extraction, followed by a SARSA-based online Q-learning algorithm to refine classification accuracy and resolve state overlap issues. During training, facial datasets are processed to extract critical features, and a state-action value table is constructed to guide decision-making during testing. This reinforcement-driven learning enables the system to dynamically update its policy based on the most rewarding actions, improving adaptability and performance. Experimental results demonstrate that the proposed approach enhanced traditional models in terms of recognition accuracy, classification efficiency, and training speed. Integrating optimized feature selection and policy learning mechanisms makes the model a promising solution for real-time and resource-efficient face recognition applications. |
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| Keywords: |
classification; face recognition; feature extraction; learning agent; reward; SARSA |
| College: |
Faculty of Science and Engineering |
| Funders: |
Swansea University. Grant Number: RS718;
Engineering and Physical Sciences Research Council. Grant Number: EP/W020408/1 |
| Issue: |
1 |