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V. Suganthi, M.Yogeshwari
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- Assistant Professor, Assistant Professor, Department of Computer Science, Chevalier T. Thomas Elizabeth College for Women, Chennai, Department of Information Technology, School of computing sciences, VISTAS, Chennai, Tamil Nadu, Tamil Nadu, India, India
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Abstract
nThis research introduces an openly accessible deep learning-based framework designed for facial recognition. The system encompasses five key stages: face segmentation, detection of facial features, face alignment, embedding, and classification. Deep learning methods are employed for the extraction of fiducial points and embedding within the system. For the classification task, a Support Vector Machine (SVM) is utilized due to its efficiency in both training and inference phases. Notably, the system achieves a facial features detection error rate of 0.12103, demonstrating proximity to state-of-the-art algorithms. Additionally, it achieves a remarkable face recognition error rate of 0.05. By integrating advanced deep learning methods for extracting fiducial points and embedding, the system gains enhanced robustness and effectiveness. Importantly, the system is capable of real-time operation, providing timely and efficient facial recognition. This study marks a noteworthy stride in advancing facial recognition systems, demonstrating commendable accuracy and real-time functionality.
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Keywords: Facial recognition, advanced learning, facial attributes, neural computing, pattern identification
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Image Processing & Pattern Recognition Progress(joipprp)]
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References
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Journal of Image Processing & Pattern Recognition Progress
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | February 16, 2024 | |
| Accepted | March 8, 2024 | |
| Published | April 3, 2024 |
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