A Review: Video Face Recognition under Occlusion
Keywords:
Face Detection, Face Recognition, Facial Occlusion, Streaming VideoAbstract
Identifying faces in images is easier but face identification in videos is more difficult than that in images because of low resolution, occlusion, non-rigid deformations, large motion, complex background and other uncontrolled conditions make the results of face detection and recognition unreliable. It is a challenging problem due to the huge variation in the appearance of faces in video to achieve accuracy. The main objective of proposed system is to efficiently identify faces even in case of occlusion like glasses, etc. which results in accuracy of system. Facial occlusions, due for example to sunglasses, hats, scarf, beards etc., can significantly affect the performance of any face recognition system. Unfortunately, the presence of facial occlusions is quite common in real-world applications especially when the individuals are not cooperative with the system such as in video surveillance scenarios. While there has been an enormous amount of research on face recognition under pose/illumination changes and image degradations, problems caused by occlusions are mostly overlooked. The focus of this paper is thus on facial occlusions, and particularly on how to improve the recognition of faces occluded by sunglasses and scarf. We propose an efficient approach which demonstrates state-of-the-art performance on streaming video face recognizing in various genres of videos and label them with the corresponding relevant names.
References
[ 1] Jeong-Seon Park, You Hwa Oh, Sang Chul Ahn , and Seong-Whan Lee, Sr. Memb “Glasses Removal from Facial Image Using Recursive Error Compensation”. IEEE Transaction on Pattern Analysis And Machine Intelligence.Vol. No.27 2012
Maria De Marsico, Member, IEEE, Michele Nappi, and Daniel Riccio “Face Recognition Against Occlusions and Expression Variations . IEEE Transaction on System , Man, And Cybernetics And Humans, VOL. 40, NO. 1, January 2010
Zafar G. Sheikh, V. M. Thakare, S. S. Sherekar “Advances in Face Detection Techniques in Video”2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing.
Minglan Sheng, Zhangli Lan “Face Detection in Video Sequence with Complex Background” Proceedings of 2008 IEEE International Conference on electronics and Automation.
Rabia Jafri and Hamid R. Arabnia* “A Survey of Face Recognition Techniques” International Journal of Information Processing Systems, June 2009
Face Detection System on Ada boost Algorithm Using Haar Classifiers M. Gopi Krishna,A. Srinivasulu, Prof (Dr.) T.K.Basak International Journal of Modern Engineering Research (IJMER) Nov-Dec. 2012
“Improving the Recognition of Faces Occluded by Facial Accessories “ Rui Min Multimedia Communications Dept. Abdenour Hadid Machine Vision Group from University of Oulu, Finland.
B.G. Park, K.M. Lee, and S.U. Lee, “Face recognition using face-ARG matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 12, pp. 2005.
Ali Javed ,Face Recognition Based on Principal Component Analysis J. Image, Graphics and Signal Processing, Feb 2013 .
Ole Helvig Jensen Kongens Lyngby “Implementing the Viola-Jones Face Detection Algorithm”. Kongens Lyngby 2008 in Proc. ICME, 2011, pp. 1–6.
Zafar G. Sheikh, V. M. Thakare, S. S. Sherekar 2nd IEEE International Conference on Parallel, Distributed and Grid Computing “Advances in Face Detection Techniques inVideo”.
W. Zhang, S. Shan, X. Chen, and W Gao, "Local Gabor Binary Patterns Based on Kullback–Leibler Divergence for Partially Occluded Face Recognition," Signal Processing Letters, IEEE , vol.14, no.11, pp.875- 878, Nov. 2007.
P. Viola and M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," Proc. Conf. Computer Vision and Pattern Recognition, pp. 511-518, 2001.
J. Yang and A.Waibel. A real-time face tracker. In Proceedings of the Third IEEE Workshop on Applications of Computer Vision, pages 142-147, Sarasota, FL, 1996.
Mikolajczyk, K.; Choudhury, R.; Schmid, C. ,Face detection in a video sequence-a temporal approach, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol 2, pp: 96-101, 2001.
Zhenqiu Zhang; Potamianos, G.; Ming Liu; Huang, T., Robust Multi-View Multi-Camera Face Detection inside Smart Rooms Using Spatio-Temporal Dynamic Programming, 7th International Conference on Automatic.
G. Bradski. Computer vision face tracking for use in a perceptual user interface. Technical Report Q2, Intel Corporation, Microcomputer Research Lab, Santa Clara, CA, 1998.
H. Schneiderman and T. Kanade. "A statistical method for 3d object detection applied to faces and cars". In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2000.
J. Feraud, O. Bernier, and M. collobert. "A fast and accurate face detector for indexation of face images". In Proc. Fourth IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 52-59, 1998.
S. Gong, S.McKenna, and J.Collins. "An investigation into face pose distribution". In Proc. IEEE International Conference on Face and Gesture Recognition, Vermont,1996.
ZhenQiu Zhang; Long Zhu; Li, S.Z.; HongJiang Zhang,Real-time multi-view face detection, Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp:142-147,2002.
P. Viola and M.J. Jones, "Robust real-time object detection", IEEE ICCV Workshop on Statistical and Computational Theories of Vision. Vancouver, Canada. July 13, 2001.
S. Z. Li, Z. Q. Zhang, "FloatBoost Learning and Statistical Face Detection", IEEE Transactions on Pattern
Analysis and Machine Intelligence, VOL. 26, NO. 9, September, 2004.
Yan Wang; Yanghua Liu; Linmi Tao; Guangyou Xu ,Real-time multi-view face detection and pose estimation in video stream, 18th International Conference on Pattern Recognition ,pp: 354 - 357,2006.
M. Nakamura, H. Nomiya and K. Uehara, "Improvement of boosting algorithm by modifying the weighting rule", Annals of Mathematics and Artificial Intelligence, 41:95-109,2004.
C.J. Edward, C.J. Taylor, and T.F. Cootes, "Learning to Identify and Track Faces in an Image Sequence," Proc. Int'l Conf. Automatic Face and Gesture Recognition, pp. 260-265, 1998.
J. Yang and A.Waibel. A real-time face tracker. In Proceedings of the Third IEEE Workshop on Applications of Computer Vision, pages 142-147, Sarasota, FL, 1996.
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