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| تعداد مقالات | 2,200 |
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A Siamese network-based Xception for Face Recognition | ||
| Journal of Electrical and Computer Engineering Innovations (JECEI) | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 20 خرداد 1404 | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22061/jecei.2025.11780.834 | ||
| نویسندگان | ||
| A. Habibi؛ M. Afrasiabi* ؛ M. Chaparian | ||
| Department of Computer Engineering, Hamedan University of Technology, Hamedan, Iran. | ||
| تاریخ دریافت: 05 اسفند 1403، تاریخ بازنگری: 20 اردیبهشت 1404، تاریخ پذیرش: 16 خرداد 1404 | ||
| چکیده | ||
| Background and Objectives: Facial recognition technology has become a reliable solution for access control, augmenting traditional biometric methods. It primarily focuses on two core tasks: face verification, which determines whether two images belong to the same individual, and face identification, which matches a face to a database. However, facial recognition still faces critical challenges such as variations in pose, illumination, facial expressions, image noise, and limited training samples per subject. Method: This study employs a Siamese network based on the Xception architecture within a transfer learning framework to perform one-shot face verification. The model is trained to compare image pairs rather than classify them individually, using deep feature extraction and Euclidean distance measurement, optimized through a contrastive loss function. Results: The proposed model achieves high verification accuracy on benchmark datasets, reaching 97.6% on the Labeled Faces in the Wild (LFW) dataset and 96.25% on the Olivetti Research Laboratory (ORL) dataset. These results demonstrate the model’s robustness and generalizability across datasets with diverse facial characteristics and limited training data. Conclusion: Our findings indicate that the Siamese-Xception architecture is a robust and effective approach for facial verification, particularly in low-data scenarios. This method offers a practical, scalable solution for real-world facial recognition systems, maintaining high accuracy despite data constraints. | ||
| کلیدواژه‌ها | ||
| Face Recognition؛ Convolutional Neural Networks؛ Siamese Network؛ Transfer Learning؛ Small-Sample Dataset | ||
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آمار تعداد مشاهده مقاله: 8 |
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