Advancements in Deep Learning for Robust Face Feature Extraction

The integration of face recognition technology in contemporary biometric systems occurs because it helps multiple security applications alongside personalized service functions. Recent deep learning applications contribute superior features which result in efficient processing and very accurate face recognition results. This paper investigates deep learning techniques applied to face feature extraction through investigations of CNNs along with their evolution alongside Siamese and Triplet networks and pretrained networks as well as attention concepts and 3D recognition and generative adversarial network approaches (GANs). This work studies how different methods impact actual field performance of facial recognition systems within challenging conditions that include changing lighting conditions and partial blocked views and different poses. The paper concludes the discussion by specifying future research directions for face recognition technology development and its improvement potential.