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Bimonthly Since 1986 |
ISSN 1004-9037
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Publication Details |
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
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FACILE EYE DEFECT DETECTION
Dr. R. Kalaivani1, Mr. M.Karthik Kannan2, Mr. S. Mahesh kumar3 Mr. A. Richard Wilson4
Journal of Data Acquisition and Processing, 2024, 39 (1): 894-899 .
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Abstract
This paper introduces a robust approach for eye detection in images, combining traditional techniques and deep learning. Initial preprocessing enhances image quality, followed by Haar- like features and AdaBoost for candidate region detection, optimizing computational efficiency. Subsequent refinement employs a convolutional neural network trained on annotated data for precise eye localization, ensuring resilience to various image conditions. Experimental validation on benchmark datasets demonstrates competitive performance and real-time suitability for applications such as gaze tracking and driver assistance. The proposed method offers a balanced trade-off between accuracy, efficiency, and robustness, making it applicable to diverse computer vision tasks requiring reliable eye detection.
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