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Abstract â€“ Face recognition is a prominent field in signal processing analysis and has received considerable attention over the past decade due to its very diverse domains of application. The potential it has, led researchers and marketers in the industry to put effort into developing what would be an optimal approach and method that would eliminate all existing challenges encountered so far. These past works include knowledge and researches from pattern recognition, psychology neuroscience, computer vision, image processing, and machine learning. A lot of published papers focused on explaining ways to overcome difference factors (such as illumination, expression and occlusion) and attain better recognition rate. A robust technique against uncontrolled practical cases with different factors simultaneously still need to be researched upon. This paper presents some of the existing approaches, techniques and challenges associated with existing face recognition systems.
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illuminationâ€™, Artif. Life Robot., Vol. 12, Issue 1, pp. 33â€“37
Batur, A.U., Hayes, M.H. (2004): â€˜Segmented linear subspaces for illumination robust face
recognitionâ€™, Int. J. Comput. Vis., Vol. 57, Issue 1, pp. 49â€“66
Belhumeur, V., Hespanda, J., Kiregeman, D., (1997): â€˜â€™Eigenfaces vs. fisherfaces:
recognition using class specific linear projectionâ€, IEEE Trans. on PAMI, Vol. 19, pp. 711 -720
Blanz, V., Grother, P., Phillips, P.J., Vetter, T. (2005): â€˜Face recognition based on frontal
views generated from non-frontal images â€™. IEEE Conf. on Computer Vision and Pattern Recognition, San Diego, CA, USA, pp. 454 â€“461
Ã‡arÄ±kÃ§Ä±, M., and Ã–zen, F.,(2012): â€˜â€™A Face Recognition System Based on Eigenfaces
Methodâ€™â€™, Procedia Technology. Pp 118-123.
Chaoyang, Z., Zhaoxian, Z., Hua, S., and Fan, D. (2012): â€˜â€™Comparison of Three Face
Recognition Algorithmsâ€™â€™. International Conference on Systems and Informatics. Pp 1896-1900.
Chen, T., Yin, W., Zhou, X., Comaniciu, D., Huang, T. (2006): â€˜Total variation models for
variable lighting face recognitionâ€™, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 28, Issue 9, pp. 1519â€“1524
Cordiner, A.; Ogunbona, P.; Wanqing Li (2009): "Face detection using generalized
integral image features," Image Processing (ICIP), 16th IEEE International Conference on , Vol.7, Issue 10., pp.1229 â€“ 1232.
Chen, X., Zhi-Hua, S., Liu, Z. J (2009): â€˜Face recognition under occlusions and variant
expressions with partial similarityâ€™, IEEE Trans. Inf. Forensics Sec., Vol. 4, Issue 2, pp. 217 â€“230
Chen, W., Er, M.J., Wu, S. (2006): â€˜Illumination compensation and normalization forrobust
face recognition using discrete cosine transform in logarithm domainâ€™,IEEE Trans. Syst. Man Cybern. B, Cybern., Vol. 36, Issue 2, pp. 458â€“466
Hong D., Ruohe Y., Kunhui L. (2008): â€˜â€™Research on Face Recognition Based on
PCAâ€, IEEE. IEEE Conference on Industrial Electronics and Applications.
Han, H., Shan, S., Chen, X., Gao, W. (2013): â€˜A comparative study on illumination
preprocessing in face recognitionâ€™, Pattern Recognit., Vol. 46, Issue 6, pp. 1691â€“1699
Ishiyama, R., Hamanaka, M., Sakamoto, S. (2005): â€˜An appearance model constructed on
3D surface for robust face recognition against pose and illumination variationsâ€™, IEEE Trans. Syst. Man Cybern. C, Appl. Rev., Vol. 35, Issue 3, pp. 326â€“334
Jinli, S., Song-Chun, Z., Shiguang, S., Xilin, C. (2011): â€˜A compositional and dynamic
model for face aging â€™, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 32, Issue 3, pp. 385 â€“401
Juwei, L., Plataniotis, K.N, and Venetsanopoulos, A.N.(2005): â€˜â€™Regularization Studies of
Linear Discriminant Analysis in Small Sample Size Scenarion with Application to Face Recognition. Pattern Recognition Letterâ€™â€™. Vol. 26, Issue 2, pp. 181-191.
Kumar, R., Barmpoutis, A., Banerjee, A., Vemuri, B. (2011): â€˜Non-Lambertian reï¬‚ectance
modeling and shape recovery of faces using tensor splinesâ€™, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 33, Issue 3, pp. 553â€“567
Lih-Heng, C., Aslleh, S.H., and Chee-Ming, T. (2009): â€˜â€™PCA,LDA and Neural Network
for Face Identificationâ€™â€™. IEEE Conference on Industrial Electronics and Applications. pp 1256-1259
Lienhart and J. Maydt (2002): â€˜â€™An Extended Set of Haar-like Features for Rapid Object
Detectionâ€™â€™. IEEE ICIP.
Lanitis, A., Taylor, C. J., Cootes, T.F. (2002): â€˜Toward automatic simulation of aging effects on face images â€™, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 24, Issue 4, pp. 442 â€“455
Levine, M., Yu, Y.(2006): â€˜Face recognition subject to variations in facial expression,
Illumination and pose using correlation filters â€™, Computation Visual. Image Understanding., Vol. 104, Issue 1, pp. 1 â€“15
Lee, K.C., Ho, J., Kriegman, D. (2005): â€˜Acquiring linear subspaces for face recognition
under variable lightingâ€™, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 27, Issue 5, pp. 684â€“698
Liu, D., Lam, K., Shen, L (2005).: â€˜Illumination invariant face recognitionâ€™, Pattern
Recognition., Vol. 38, Issue 10, pp. 1705 â€“1716
Lin, J., Ming, J., Crookes, D. (2011): â€˜Robust face recognition with partial occlusion, illumination variation and limited training data by optimal feature selectionâ€™, IET Comput. Vis., Vol. 5, Issue 1, pp. 23 â€“32
Liu, C., Chen, W., Han, H., Shan, S. (2013): â€˜Effects of image preprocessing on face
matching and recognition in human observersâ€™, Appl. Cogn. Psychol. (ACP), Vol. 27, Issue 6, pp. 718â€“724.
Monali C., Gauresh V., Dhairya T., Malay S., and Amit K. (2015): â€˜â€™Intelligent
Surveillance and Security Systemâ€–â€™â€™, Vol. 3, Issue 3, pp. 2291- 2299.
Park, U., Tong, M., Jain, A.K (2010): â€˜Age-invariant face recognitionâ€™, IEEE Trans.Pattern
Anal. Mach. Intell., Vol. 32, Issue 5, pp. 947 â€“954
Patel, V., Wu, T., Biswas, S., Phillips, P., Chellappa, R. (2012): â€˜Dictionary-based face
recognition under variable lighting and poseâ€™, IEEE Trans. Inf. Forensics Sec. Vol. 7, Issue 3, pp. 954â€“965
Paul V., Michael J. (2011): "Rapid object detection using a Boosted Cascade of
Simple features" Conference on Computer.
Qing, L., Shan, S., Gao, W., Du, B. (2005): â€˜Face recognition under generic illumination
based on harmonic relightingâ€™, Int. J. Pattern Recognit. Artif. Intell., Vol. 19, Issue 4, pp. 513â€“531
Smith, W., Hancock, E. (2008): â€˜Facial shape-from-shading and recognition using principal
geodesic analysis and robust statisticsâ€™, Int. J. Comput. Vis., Vol. 76, Issue 1, pp. 71â€“ 91
Sushma J., Sarita S. B., Rakesh S. J., (2011): â€comparison between face recognition algorithm eigenfaces, fisherfaces and elastic bunch graph matchingâ€, Vol. 2, No. 7, Journal of Global Research in Computer Science
Stewart, T., (2003): â€œComparison of holistic and feature based approaches to face
recognition.â€ Masterâ€™s Thesis, Royal Melbourne Institute of Technology University, Melbourne, Victoria, Australia
Tan, X., Chen, S., Zhou, Z., Zhang, F (2005).: â€˜Recognizing partially occluded, expression
variant faces from single training image per person with SOM and soft K-NN ensemble â€™, IEEE Trans. Neural Network., Vol. 16, No. 4, pp. 875 â€“886
Wang, J., Shang, Y., Su, G., Lin, X. (2006): â€˜Age simulation for face recognitionâ€™. 18th Int.
Conf. Pattern Recognition, Hong Kong, China, pp. 913 â€“916
Xueguang, W., and Xiaowei, D. (2009): â€˜â€™Study on Algorithm of Access Control System
Based on Face Recognitionâ€™â€™. International Colloquium on Control and Management.. ISECS, pp. 336-338.
Xiaowei Z., Xiujuan C. (2013): ,"Context Constrained Facial Landmark Localization
Based on Discontinuous Haar-like Feature" International Conference on Computer Vision, pp. 53-63.
Yang, M., David J. K., and Narendra A. (2002): "Detecting faces in images: A survey."
Pattern Analysis and Machine Intelligence, IEEE Transactions on Vol. 24, Issue 1, pp. 34- 58.
Zhang, X., Gao, Y.(2009): â€˜Face recognition across pose: a review â€™, Pattern Recognition.,
Vol. 42, Issue 11, pp. 2876 â€“2896
Zhengming L., Lijie X., Fei T,, (2010): "Face detection in complex background based
on skin color features and improved AdaBoost algorithms," Progress in Informatics and Computing (PIC), IEEE International Conference on , Vol.2, , pp.723,727.