A Deep Siamese ResNet-50 Framework with Triplet loss for High-Precision Face Verification

Abstract This study proposes a deep learning framework for image verification that integrates a Siamese ResNet-50 architecture with triplet loss to enhance feature discrimination, particularly for facial recognition tasks. The model leverages the powerful residual connections of ResNet-50 for robust feature extraction and replaces the conventional contrastive loss with triplet loss to optimize inter-class and intra-class distances in the learned embedding space. Advanced training strategies, including the Adam optimizer, Cosine Annealing learning rate scheduling, and weight decay regularization, are employed to stabilize convergence and improve generalization. The proposed model is evaluated on the challenging Labelled Faces in the Wild (LFW) dataset, achieving 88.33% accuracy, an F1-score of 0.8828, and an AUC-ROC of 0.96. These results outperform baseline architectures such as Siamese VGG16, ResNet-34, and ConvNextTiny, while maintaining a favorable computational complexity. Additional analyses including ROC curves, mean Average Precision (mAP), inference time, and calibration performance demonstrate the model’s superior balance between accuracy, speed, and deployment readiness. These findings highlight the model's strong potential for practical biometric authentication systems and few-shot learning applications.