The depth CNN face recognition system by using Eigen values features

The depth CNN face recognition system consists of four main steps: face detection, localization, feature extraction, and matching. Face detection and localization are based on existing methods but have been modified to achieve faster computation speed and higher accuracy. For feature extraction, deep learning techniques are employed. We propose using Extreme Feature Mapping as the non-linear function in the CNN and introduce Residual Networks (ResNet) to develop a deep neural network with a total parameter count of just over eight million. With a training dataset comprising over a million images of about twenty thousand faces, we can extract 256-dimensional face feature vectors. In testing with the LFW face database, the system achieves a recognition accuracy of up to 98% from face detection, localization, feature extraction, to recognition.