Attention-Enhanced ArcFace-Based Deep Learning Framework for Unconstrained Face Recognition
Face recognition in unconstrained environments remains a challenging problem in computer vision due to variations in pose, illumination, expression, and occlusion. This paper proposes a novel attention-enhanced ArcFace-based deep learning framework that integrates a Residual CNN backbone with Convolutional Block Attention Module (CBAM) and ArcFace loss for robust face recognition. Unlike existing approaches that rely on large-scale external pretraining datasets, the proposed framework is trained exclusively on the Labelled Faces in the Wild (LFW) dataset, demonstrating data-efficient learning. The system is evaluated on both 1:1 verification and 1:N identification protocols. Experimental results demonstrate superior performance with verification accuracy of 95.70%, identification accuracy of 89.75%, ROC-AUC of 99.16%, and True Positive Rate (TPR) of approximately 92% at a 1% False Positive Rate (FPR). The novelty lies in the synergistic integration of attention mechanisms with angular margin-based metric learning, achieving competitive performance without external pretraining. Comparative analysis with state-of-the-art methods including DeepFace, FaceNet, VGGFace, SphereFace, and baseline ArcFace validates the effectiveness of the proposed attention-guided approach for unconstrained face recognition tasks.