RECOGNITION OF MULTI-VIEW HUMAN FACES BASED ON MACHINE INTELLIGENCE USING KLT ALGORITHM
Keywords:Face Recognition, Feature Extraction, KLT Algorithm, Local Binary Pattern
Nowadays Image Processing has become a proficient domain due to the prolific techniques like face detection and face recognition. They play an important role in our society due to their use in wide range of applications such as surveillance, security, banking, and multimedia. One of major challenges faced in this technique of face recognition is difficulty in handling arbitrary pose variations in three dimensional representations. In video retrieval system, many approaches have been developed for recognition across pose variations and to assume the face poses to be known. These constraints made it semi-automatic. In this paper we propose a fully automatic method for multi-view face recognition of improving the accuracy or efficiency using local binary patterns. It uses tree-based data structure to create sub-grids. In this system we use KLT algorithm to detect and extract features automatically by using Eigen vectors and estimation of hessian value.
R. Jafri and H. R. Arabnia, A Survey of Face Recognition Techniques Journal of Information Processing Systems, vol. 5, no. 2, pp. 41–68, Jun. 2009. DOI: https://doi.org/10.3745/JIPS.2009.5.2.041
L. Ding, X. Ding, and C. Fang , Continuous pose normalization for pose-robust face recognition, IEEE Signal Processing Letters, 19(11), 2012 ,721–724. DOI: https://doi.org/10.1109/LSP.2012.2215586
M. Turk,A. Pentland , Eigen faces for recognition, Cognitive Neuroscience, vol.3, no.1, pp.71–86, 1991. DOI: https://doi.org/10.1162/jocn.19126.96.36.199
T. Ojala, M. Pietikainen, and T. Maenpaa , Multi resolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans. PAMI, 24(7): 971–987, 2002. DOI: https://doi.org/10.1109/TPAMI.2002.1017623
Rafel.c.Gonzalez, Richard E.Woods digital image processing(Pearson education inc.)
Rajkamal kishor Gupta, Object detection and tracking in video image, Department of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, India.
Naotoshi Seo, simultaneous multi-view face tracking and recognition in video using particle filtering, the Graduate School of the University of Maryland, College Park in partial fulfillment of the requirements for the degree of Master of Science,2009.
Saeed U., Matta F. and Dugelay J.-L., Person recognition based on head and mouth dynamics, in IEEE Proceedings on Multimedia Signal Processing, pag. 29-32, October 2006. DOI: https://doi.org/10.1109/MMSP.2006.285262
Storring, M.—Andersen, H. J.—Granum, E. , Estimation of the Illuminant Color from Human Skin Color, Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition, 2000.
Schwerdt, K.—Crowley, J. L.: Robust Face Tracking Using Color. Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition, 2000.
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