GMM based approach for human face verification using relative depth features

Image based face detection and verification is a well researched topic and has found many applications. The limitation with image based techniques is that 3D real world objects are mapped on to a 2D plane, which causes loss of 3D features of real objects. Multiple systems are available in literature to estimate depths of objects viz. stereo images, 3D and 2.5D scanners, etc. However, these systems require additional hardware and are expensive. In the proposed approach, a colored pattern of light scans the face and video is captured simultaneously using a optical camera. The colored pattern is detected in every frame using Gaussian Mixture Model (GMM). Due to non-planarity of the face, the pattern gets distorted. Distortion in the pattern is calculated to obtain 3D information. In this way, 3D information of face is derived from the 2D frames. Based on this data, 3D or depth features are calculated, which are then used for face verification. This approach is robust and handles variations due to light and insignificant periodic changes of background objects.