Document Type : Original Research Paper

Authors

• A. Habibi
• M. Afrasiabi
• M. Chaparian

Department of Computer Engineering, Hamedan University of Technology, Hamedan, Iran.

Abstract

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.

Keywords

• Face Recognition
• Convolutional Neural Networks
• Siamese Network
• Transfer Learning
• Small-Sample Dataset

Main Subjects

• Image Processing