Reliable Holoscopic 3D Face Recognition

Face recognition has made significant progress in recent years. It has been successfully applied to many applications from security entrance systems for staff identification to visual surveillance. However, the performance of current systems are highly affected by factors such as subject's head orientation, facial expressions, and face wear-on. In order to address this challenge, researchers usually resort to more training data or more complex recognition models. We believe the secret lies in using a more rich data source. Therefore, in this paper, we use a holoscopic 3D (H3D) imaging system. To show the effectiveness of the sensor, we use it with a simple back propagation neural network (BPNN) classifier. We illustrate the accuracy of the system through an experiment and show that it has high accuracy and is robust against challenges aforementioned. The proposed approach consists of three steps: (a) H3D face images are captured using the single aperture H3D camera. (b) Wavelet features are extracted and selected from multiperspective viewpoint images obtained from the H3D images. (c) A simple artificial neural network (ANN) is used for reliable face recognition.