MODELING OF LIGHT ILLUMINATION FIELD ON MICRO-EXPRESSION FOR FACE RECOGNITION APPLICATION
DOI:
https://doi.org/10.22159/ajpcr.2017.v10s1.19653Keywords:
Facial Recognition, Face detection, Algorithm Design & AnalysisAbstract
the feature points and thereby degrades the recognition rate. Though past techniques in the same area often requires manual setting of thresholding parameters but in this study the presented technique solves the problem of variation in light illumination by modeling the light field on facial micro expression for improving performance of the face recognition. The performance of the method is compared with the other techniques based on two benchmark datasets namely: CMU PIE & Multi-PIE. Â Â
Downloads
References
T. Ahonen, A. Hadid, and M. Pietikainen. Face description with local binary patterns: Application to face recognition. IEEE PAMI, 28(12):2037–2041, 2006.
W. Zhang, S. Shan, W. Gao, X. Chen, and H. Zhang. Local Gabor binary pattern histogram sequence (lgbphs): A novel non-statistical model for face representation and recognition.
ICCV, pages 786–791, 2005.
X. Tan and B. Triggs. Enhanced local texture feature sets for face recognition under difficult lighting conditions. AMFG, pages 168–182, 2007.
A. Shashua and T. Riklin-Raviv. The quotient image: Class based re-rendering and recognition with varying illuminations. IEEE PAMI, 23(2):129–139, 2001.
H. Wang, S. Li, and Y. Wang. Generalized quotient image. In CVPR, 2004.
T. Chen, W. Yin, X. S. Zhou, D. Comaniciu, and T. Huang. Total variation models for variable lighting face recognition. IEEE PAMI, 28:1519–1524, 2006.
V. Blanz and T. Vetter. Face recognition based on fitting a 3D morphable model. IEEE PAMI, 25(9):1063–1074, 2003.
Y. Wang, L. Zhang, Z. Liu, G. Hua, Z. Wen, Z. Zhang, and D. Samaras. Face relighting from a single image under arbitrary unknown lighting conditions. IEEE PAMI,
(11):1968–1984, 2009.
L. Zhang and D. Samaras. Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics. IEEE PAMI, 28(3):351–363, 2006.
T. Sim and T. Kanade. Combining models and exemplars for face recognition: An illuminating example. CVPR Wkshp Models vs Exemplars in Computer Vision, 2001.
K.-C. Lee and B. Moghaddam. A practical face relighting method for directional lighting normalization. AMFG, 2005.
T. Sim, S. Baker, and M. Bsat. The CMU Pose, Illumination, and Expression database. IEEE PAMI, 25(12):1615–1618, 2003.
R. Gross, I. Matthews, J. Cohn, S. Baker, and T. Kanade. The CMU Multi-Pose, Illumination, and Expression (Multi-PIE) face database. Tech Rep TR-07-08, CMU, 2007.
K.-C. Lee, J. Ho, and D. J. Kriegman. Acquiring linear subspaces for face recognition under variable lighting. IEEE PAMI, 27(5):684–698, 2005.
A. Barmpoutis, R. Kumar, B. C. Vemuri, and A. Banerjee. Beyond the lambertian assumption: A generative model for apparent BRDF fields of faces using anti-symmetric tensor splines. CVPR, 2008.
Rai, Ankush. "Attribute Based Level Adaptive Thresholding Algorithm (ABLATA) for Image Compression and Transmission." Journal of mathematics and computer science, 12 (2014), 211-218.
Rai, Ankush. "An Introduction of Smart Self-learning Shell Programming Interface." Journal of Advances in Shell Programming 1.2 (2015): 3-6.
Rai, Ankush. "Dynamic data flow based spatial sorting method for GPUs: Software based autonomous parallelization." Recent Trends in Parallel Computing 1.1 (2014): 15-18.
Published
How to Cite
Issue
Section
The publication is licensed under CC By and is open access. Copyright is with author and allowed to retain publishing rights without restrictions.
