Automatic facial recognition is a widely used task in the field of computer vision, which is very easy for a human, but very challenging for computers. In this project, a face classification algorithm based on face recognition feature vectors is proposed. Firstly, face detection and preprocessing are performed on the input images. Finally, deep learning methods are used to classify the face dataset images. The proposed method has achieved a recognition rate of 99.2% and 98.7% on the mobile net algorithm to predict the human face.
Face anti-spoofing is an important task in full-stack face applications including face detection, verification, and recognition. Previous approaches build models on datasets which do not simulate the real-world data well (e.g., small scale, insignificant variance, etc.). Existing models may rely on auxiliary information, which prevents these anti spoofing solutions from generalizing well in practice. In this project, we present a data collection solution along with a data synthesis technique to simulate digital medium-based face spoofing attacks, which can easily help us obtain a large amount of training data well reflecting the real-world scenarios.