Abstract
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.
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Acknowledgment
Authors would like to acknowledge the financial assistance from Minister of Electronics and Information Technology (MeitY) under Visvesvaraya PhD Scheme for carrying out research work at Goa University. Authors are also thankful to Ms. Bhagyada Pai Kane, Ms. Shweta Sawal Desai and Mr. Saurabh Vernekar (Post graduate students, Department of Electronics, 2015 batch) for their support in the RGBD database collection and to all the subjects for their valuable participation.
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Gaonkar, A.A., Gad, M.D., Vetrekar, N.T., Tilve, V.S., Gad, R.S. (2017). Experimental Evaluation of 3D Kinect Face Database. In: Mukherjee, S., et al. Computer Vision, Graphics, and Image Processing. ICVGIP 2016. Lecture Notes in Computer Science(), vol 10481. Springer, Cham. https://doi.org/10.1007/978-3-319-68124-5_2
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DOI: https://doi.org/10.1007/978-3-319-68124-5_2
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