Advanced 3D face anti-spoofing system using hybrid deep neural network and optimization techniques

ace recognition technology is used everywhere, including mobile security, surveillance, and online payments, to authenticate a person's identity. Spoof faces, like printed photos, videos, 3D masks, and deep fake-generated faces, can mislead such systems. The goal of this research is to improve Face Anti-Spoofing (FAS) techniques to improve facial recognition security, accuracy, and efficiency. The study suggests a 3D Face Anti-Spoofing Model incorporating Dense Squeeze and Excitation Networks and a Neighbourhood-Aware Kernel Adaptation (NAKA) process for identification of fine details and textures on a person's face. A Lightweight Multi-Modal Deep Fusion Network is suggested for fusing different face data modalities such as RGB images, depth maps, and texture information. It is useful in face spoofing detection with higher accuracy even against advanced attacks. Deep Reinforcement Learning (DRL) is also used by the system to learn automatically and keep itself updated at all times, thus able to identify new ways of spoofing. The models are tested using standard datasets like CASIA-SURF, CelebA-Spoof, and GREAT-FASD-S based on a number of performance metrics such as accuracy, precision, recall, and error rates. Experiments demonstrate that models as proposed compare favorably against existing methods and are computationally lightweight for use in real-world applications. The study also confronts fundamental problems such as generalizable good performance over datasets, adversarial attacks resistance, and security vs. usability. Grounded on multi-modal fusion, vi attention, and reinforcement learning, the study gives contributions towards robust, light-weight, and explainable face anti-spoofing approaches. These can be applied in high-security applications like banking, border control, and mobile authentication. The outcome will help to introduce biometric security and protect against future face spoofing attacks of novel forms.