Enhancing Face Recognition Systems by Focusing on Pose Robustness

Convolutional Neural Networks (CNNs) have revolutionized various computer vision tasks, including classification, semantic segmentation, and action recognition. However, they exhibit limitations, particularly in accurately localizing faces and handling varying head poses. This paper aims to investigate and propose a robust face recognition system capable of effectively addressing these challenges. Current CNN-based systems often struggle to localize faces accurately without relying heavily on appearance cues, leading to limited performance in recognizing faces across different poses. Our proposed approach focuses on overcoming these limitations by developing a system that can effectively classify face localization while maintaining robustness to varying head poses. Leveraging datasets like Labelled Faces in the Wild (LFW), which contain images captured in uncontrolled environments with diverse poses, we aim to design a system that can reliably recognize individuals regardless of their head orientations. By exploring advanced techniques beyond CNNs and integrating pose estimation and recognition methods, our objective is to advance the state-of-the-art in face recognition systems, enabling accurate and robust recognition across a wide range of head poses.