POSE-Inclusive Face Recognition:‎ Addressing The Influence of Face Angle in ‎Person Identification - Harshitha, C J; Bharathi, R. K.; P, Rakshitha (International Journal of Basic and Applied Sciences, 2025)
Face recognition remains a critical task in computer vision, ‎especially in applications involving authentication, surveillance, ‎and access control. However, real-world challenges like pose ‎variations and occlusions continue to hinder recognition ‎performance. This paper presents a pose-inclusive face ‎recognition system that integrates Multi-task Cascaded ‎Convolutional Networks (MTCNN) for face detection and ‎alignment with ResNet-50 for feature extraction and ‎classification. The Labeled Faces in the Wild (LFW) dataset is ‎used, and images undergo data augmentation and normalization ‎to simulate pose diversity. Experimental evaluation ‎demonstrates that ResNet-50 significantly outperforms ‎traditional CNN models, achieving an accuracy of 99.60%. The ‎proposed approach ensures robust and scalable performance ‎in uncontrolled environments‎.
Keywords: Occluded Face Recognition; MTCNN; CNN