Experimental Evaluation of 3D Kinect Face Database

3D face recognition has gain a paramount importance over 2D due to its potential to address the limitations of 2D face recognition against the variation in facial poses, angles, occlusions etc. Research in 3D face recognition has accelerated in recent years due to the development of low cost 3D Kinect camera sensor. This has leads to the development of few RGB-D database across the world. Here in this paper we introduce the base results of our 3D facial database (GU-RGBD database) comprising variation in pose (0°, 45°, 90°, −45°, −90°), expression (smile, eyes closed), occlusion (half face covered with paper) and illumination variation using Kinect. We present a proposed noise removal non-linear interpolation filter for the patches present in the depth images. The results were obtained on three face recognition algorithms and fusion at matching score level for recognition and verification rate. The obtained results indicated that the performance with our proposed filter shows improvement over pose with score level fusion using sum rule.