We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
Reseach Article

Comparative Analysis of Face Recognition Approaches: A Survey

by Ripal Patel, Nidhi Rathod, Ami Shah
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 17
Year of Publication: 2012
Authors: Ripal Patel, Nidhi Rathod, Ami Shah
10.5120/9210-3756

Ripal Patel, Nidhi Rathod, Ami Shah . Comparative Analysis of Face Recognition Approaches: A Survey. International Journal of Computer Applications. 57, 17 ( November 2012), 50-69. DOI=10.5120/9210-3756

@article{ 10.5120/9210-3756,
author = { Ripal Patel, Nidhi Rathod, Ami Shah },
title = { Comparative Analysis of Face Recognition Approaches: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 17 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 50-69 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number17/9210-3756/ },
doi = { 10.5120/9210-3756 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:00:45.784390+05:30
%A Ripal Patel
%A Nidhi Rathod
%A Ami Shah
%T Comparative Analysis of Face Recognition Approaches: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 17
%P 50-69
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent days, the need of biometric security system is heightened for providing safety and security against terrorist attacks, robbery, etc. The demand of biometric system has risen due to its strength, efficiency and easy availability. One of the most effective, highly authenticated and easily adaptable biometric security systems is facial feature recognition. This paper has covered almost all the techniques for face recognition approaches. It also covers the relative analysis between all the approaches which are useful in face recognition. Consideration of merits and demerits of all techniques is done and recognition rates of all the techniques are also compared.

References
  1. Proyecto Fin de Carrera, Face Recognition Algorithms.
  2. A. Golstein, L. Harmon, and A. Lest. Identification of human faces. Proceedings of the IEEE, 59:748–760, 1971.
  3. M. Fischler and R. Elschlager. The representation and matching ofpictorial structures. IEEE Transactions on Computers, C-22(1):67 –92, 1973.
  4. T. Kenade. Picture Processing System by Computer Complex and Recognition of Human Faces. PhD thesis, Kyoto University, November 1973.
  5. M. Nixon. Eye spacing measurement for facial recognition. Proceedings of the Society of Photo-Optical Instrument Engineers, SPIE,575(37):279–285, August 1985.
  6. L. Sirovich and M. Kirby, "Low-dimensional procedure for the characterizationof human faces," Journal of the Optical Society of America A - Optics, Image Science and Vision, 4(3):519–524, March 1987.
  7. M. Kirby and L. Sirovich,"Application of the karhunen-loeveprocedurefor the characterization of human faces," IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(1):103–108, 1990.
  8. M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of Cognitive Neurosicence, 3(1):71–86, 1991.
  9. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman,"Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection," in IEEE TPAMI. vol. 19, 1997, pp. 711-720.
  10. M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski,"Face recognition by independent component analysis,"IEEE Transactions on Neural Networks, Vol. 13, pp. 1450-1464, 2002.
  11. T. K. Kim, S. F. Wong, B. Stenger, J. Kittler, and R. Cipolla, "Incremental Linear Discriminant Analysis Using Sufficient Spanning Set Approximations," in IEEE CVPR, 2007, pp. 1-8.
  12. L. Wiskott, J. M. Fellous, N. Kruger, and C. von der Malsburg, "Face recognition by elastic bunch graph matching," IEEE Trans. Pattern Anal. MachineIntell. , vol. 19, pp. 775–779, July 1997.
  13. P. Jonathon Phillips, "Support Vector Machines Applied to Face Recognition", a technical report NISTIR 6241 appeared in Advances in Neural InformationProcessing Systems 11, eds. M. J. Kearns, S. A. Solla, and D. A. Cohn, MIT Press,1999.
  14. F. Samaria, "Face Recognition Using Hidden Markov Models", PhD dissertation,Trinity College, University of Cambridge.
  15. F. Samaria and A. Harter, "Parametrisation of a Stochastic Model for Human FaceIdentification", Proc. 2nd IEEE Workshop on Applications of Computer Vision, pp. 138-142, Dec. 1994.
  16. Aria V. Nefian, and Monson H. Hayes, "Hidden Markov Models for face detectionand recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence, April 1999.
  17. Aria V. Nefian, and Monson H. Hayes, "Hidden Markov Models for FaceRecognition", IEEE International Conference on Acoustic, Speech and Signal Processing 98, vol. 5, p. 2721-27224
  18. Cottrell, G. W. , Fleming, M. K. , 1990. Face recognition using unsupervisedfeature extraction. In: Proc. Intell. Neural Network Conf. , pp. 322–325.
  19. T. Cootes, C. Taylor, Statistical models of appearance for computer vision, Technical Report, University of Manchester, Imaging Science and Biomedical Engineering, Manchester M13 9PT, United Kingdom, September 1999.
  20. P. N. Belhumeur, J. Hespanha, D. J. Kriegman, Eigenfacesvs. Fisherfaces: recognition using class specific linear projection, IEEE European Conference on Computer Vision, 1996, pp. 45–58.
  21. R. Brunelli, T. Poggio, Face recognition: features versustemplates, IEEE Trans. Pattern Anal. Machine Intell. 15 (10) (1993) 1042–1052.
  22. J. Cohn, A. Zlochower, J. J. Lien, T. Kanade, Featurepointtracking by optical flow discriminates subtle differences in facial expression, Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, April 1998, pp. 396–401.
  23. B. Scholkopf, A. Smola, K. Muller, Nonlinear componentanalysis as a kernel eigenvalue problem, Neural Computation 10 (5) (1998) 1299–1319.
  24. Z. Q. Zhao, D. S. Huang, and B. Y. Sun, "Human face recognition based on multi-features using neural networks committee," Pattern Recognit. Lett. , vol. 25, no. 12, pp. 1351–1358, Sep. 2004.
  25. Chengjun Liu, "Capitalize on Dimensionality Increasing Techniques for Improving Face RecognitionGrand Challenge Performance": IEEE transactions on pattern analysis and machine intelligence, vol. 28, no. 5, may 2006.
  26. Lawrence S. , Giles C. L. , Tsoi A. C. , ands Back A. D. , 1998. Face recognition: A convolutional neural network approach. IEEE Transactions on Neural Networks, Vol. 8, pp. 98–113.
  27. Xiaofei He, Shuicheng Yan, Yuxiao Hu, ParthaNiyogi, and Hong-Jiang Zhang, "Face Recognition Using Laplacianfaces," IEEE transactions on pattern analysis and machine intelligence, vol. 27, no. 3, march 2005.
  28. Cartoux J. Y. , Lapreste J. T. , AND Richetin M. 1989. Face authentication or recognition by profile extraction from range images. In Workshop on Interpretation of 3D Scenes, pp. 194–199.
  29. Hyeonjoon Moon and P Jonathon Phillips, 'Computational and performance aspect of PCA based face recognition algorithms', Perception,2001,volume 30, pages 303-321.
  30. P. Comon, "Independent component analysis, a new concept?," SignalProcessing, vol. 36, pp. 287–314, 1994.
  31. J. Karhunen, E. Oja, L. Wang, R. Vigario, and J. Joutsensalo, "A classof neural networks for independent component analysis," IEEE Trans. Neural Networks, vol. 8, pp. 486–504, Mar. 1997.
  32. DanijelaVukadinovic and MajaPantic"Fully Automatic Facial Feature Point Detection UsingGabor Feature Based Boosted Classifiers" 2005 IEEE International Conference on Systems, Man and CyberneticsWaikoloa, Hawaii October 10-12, 2005
  33. Hong Bo Deng, Lian Wen Jin, Li-Xin Zhen, Jian-Cheng Huang "A New Facial Expression Recognition Method Bassed on Local Gabor Filter Bank and PCA plus LDA"International Journal of Information Technology Vol. 11 No. 11 2005.
  34. J. Nagi, S. K. Ahmed, F. Nagi. A MATLAB based Face Recognition System using Image Processing and Neural Networks, in Proc. of the 4th International Colloquium on Signal Processing and its Applications (CSPA), Mar. 7-9, 2008, Kuala Lumpur, Malaysia, pp. 83–88.
  35. Jing Shao, Jia-fu Jiang, Xiao-wei Liu, 'Biomimetic Pattern Face Recognition Based on DCT and LDA', Artificial Intelligence and Computational Intelligence Lecture Notes in Computer Science Volume 7004, 2011, pp 170-177
  36. ZIAD M. HAFED AND MARTIN D. LEVINE, 'Face Recognition Using the Discrete Cosine Transform', International Journal of Computer Vision 43(3), Pages:167–188, 2001
  37. Azam, M. and Javed, M. Y. ," Discrete Cosine Transform (DCT) Based Face Recognition in Hexagonal Images", Proceedings of 2nd International Conference on Computer and Automation Engineering (ICCAE 2010), 26-28, February 2010 (ISBN: 978-1-4244-5585-0), Singapore, pp. 168-170.
  38. Randa Atta and Mohammad Ghanbari, 'Low-Memory Requirement and Efficient Face Recognition System Based on DCT Pyramid', IEEE Transactions on Consumer Electronics, Vol. 56, No. 3, August 2010
  39. M. Lades, J. C. Vorbruggen, J. Buhmann, J. Lange, C. von derMalsburg, R. P. Wurtz, and W. Konen, "Distortion Invariant ObjectRecognition in the Dynamic Link Architecture," IEEE Trans. Computers, vol. 42, no. 3, pp. 300-311, Mar. 1993.
  40. S. Mika, G. Ratsch, J. Weston, B. Scholkopf, and K. R. Miller,"Fisher Discriminant Analysis with Kernels," Neural Networks for Signal Processing IX, Y. H. Hu, J. Larsen, E. Wilson, and S. Douglas, eds. , pp. 41-48, 1999.
  41. T. Cooke, "Two Variations on Fisher's Linear Discriminant forPattern Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 2, pp. 268-273, Feb. 2002.
  42. G. Baudat and F. Anouar, "Generalized Discriminant AnalysisUsing a Kernel Approach," Neural Computation, vol. 12, no. 10,pp. 2385-2404, 2000.
  43. M. H. Yang, "Kernel eigenfaces vs. Kernel Fisherfaces: FaceRecognition Using Kernel Methods," Proc. Fifth Int'l Conf. Automatic Face and Gesture Recognition, May 2002.
  44. W. Zheng, L. Zhao, and C. Zou, "A Modified Algorithm forGeneralized Discriminant Analysis," Neural Computation, vol. 16, no. 6, pp. 1283-1297, 2004.
  45. Xiaoli Zhou and BirBhanu "Integrating Face and Gait for Human Recognition at a Distance in Video":IEEE transactions on systems, man, and cybernetics—part b: cybernetics, vol. 37, no. 5, october 2007.
  46. S. Periaswamy and H. Farid, "Elastic registration in the presence of intensityvariations," IEEE Trans. Med. Imag. , vol. 22, no. 7, pp. 865–874,Jul. 2003.
  47. K. Okada, J. Steffens, T. Maurer, H. Hong, E. Elagin, H. Neven, and C. v. d. Malsburg, "The Bochum/USC face recognition system and how it fared in the FERET phase III test," in Face Recognition from Theoryto Application, H. Wechsler et al. , Eds. New York: Springer-Verlag,1998, pp. 187–205.
  48. A. Pentland, B. Moghaddam, and T. Starner, "View-based and modulareigenspaces for face recognition," in CVPR'94, 1994, pp. 84–91.
  49. D. J. Beymer, Face recognition under varying pose, in A. I. Memo, MITAI Lab, no. 1464, 1993.
  50. D. Beymer, A. Shashua, and T. Poggio, Example based image analysisand synthesis, in A. I. Memo, Artificial Intelligence Laboratory, MIT, no. 1431, 1993.
  51. T. Vetter and T. Poggio, "Linear object classes and image synthesis from a single example image," IEEE Trans. Pattern Anal. Machine Intell. , vol. 19, no. 7, pp. 733–742, 1997.
  52. V. Blanz and T. Vetter. A morphable model for the synthesisof 3D faces. In Computer Graphics Proc. SIGGRAPH'99,pages 187. 194, 1999.
  53. Chao-Kuei Hsieh, Shang-Hong Lai and Yung-Chang Chen "An Optical Flow-Based Approach to Robust Face Recognition Under Expression Variations" :IEEE transactions on image processing, vol. 19, no. 1, january 2010.
  54. E. Osuna, R. Freund, and F. Girosi. Training support vectormachines: An application to face detection. In Proc. Computer Vision and Pattern Recognition'97, pages 130–136, 1997.
  55. AshwiniKanade, AkhilKhare "A Hybrid Nueral Network Approach for Face Recognition" International Journal of Modem Engineering Research(IJMER), Vol 2,Issue. 3 May-June 2012 pp-574-578.
  56. C. -K. Hsieh, S. -H. Lai, and Y. -C. Chen, "Expression-invariant facerecognition with accurate optical flow," in Proc. PCM, Hong Kong,Dec. 11–14, 2007.
  57. A. M. Martinez, "Recognizing expression variant faces from a single sample image per class," presented at the IEEE Conf. Computer Vision and Pattern Recognition, Jun. 2003.
  58. S. Haykin, Neural Networks—A Comprehensive Foundation, second ed. Prentice Hall, 1999.
  59. P. J. Phillips, W. T. Scruggs, A. J. O'Toole, P. J. Flynn, K. W. Bowyer, C. L. Schott, and M. Sharpe, "FRVT 2006 and ICE 2006 Large-Scale Results," Technical Report NISTIR 7408, Nat'l Inst. of Standards and Technology,Mar. 2007.
  60. H. Ling, S. Soatto, N. Ramanathan, and D. Jacobs, "A Study of FaceRecognition as People Age," Proc. IEEE Int'l Conf. Computer Vision, pp. 1-8,2007.
  61. Unsang Park, Yiying Tong, and Anil K. Jain "Age-Invariant Face Recognition": IEEE transactions on pattern analysis and machine intelligence, vol. 32, no. 5, may 2010.
  62. Anthropometry of the Head and Face, L. G. Farkas, ed. Lippincott Williams &Wilkins, 1994.
  63. N. Nixon and P. Galassi, The Brown Sisters, Thirty-Three Years. The Museumof Modern Art, 2007.
  64. J. Han and B. Bhanu, "Individual recognition using gait energy image,"IEEE Trans. Pattern Anal. Mach. Intell. , vol. 28, no. 2, pp. 316–322,Feb. 2006.
  65. Xiaoli Zhou and BirBhanu "Feature Fusion Of Side Face And Gait for Video Based Human-identification" Volume 41, Issue 3, March 2008, Pages 778–795.
  66. A. Sundaresan, A. Roy-Chowdhury, and R. Chellappa, "A hiddenMarkovmodel based framework for recognition of humans from gait sequences,"inProc. Int. Conf. Image Process. , 2003, vol. 2, pp. 93–96.
  67. P. S. Huang, C. J. Harris, and M. S. Nixon, "Recognizing humans bygait via parametric canonical space," Artif. Intell. Eng. , vol. 13, no. 4,pp. 359–366, Oct. 1999.
  68. D. Tao, X. Li, X. Wu, and S. Maybank, "Human carrying status in visualsurveillance," in Proc. IEEE Conf. Comput. Vis. and Pattern Recog. , 2006,vol. 2, pp. 1670–1677.
  69. V. Blanz and T. Vetter. Face recognition based on _tting a3d morphable model. IEEE Trans. on Pattern Analysis andMachine Intelligence, 25(9):1063. 1074, 2003.
  70. Karl B. J. Axnick1 and Kim C. Ng1 "Fast Face Recognition"
  71. A. Pentland, B. Moghaddam, T. Starner, O. Oliyide, and M. Turk. , "View-Based and ModularEigenspaces for Face Recognition", Technical Report 245, MIT Media Lab, 1993.
  72. S. Da-Rui and W. Le-Nan, "A local-to-holisticface recognition approach using elastic graphmatching" Proc. International Conference onMachine Learning and Cybernetics, Vol. 1,Page(s):240 - 242, 4-5 Nov. 2002.
  73. B. V. K. Vijaya Kumar, A. Mahalanobis and Richard Juday, Correlation Pattern Recognition, Cambridge University Press, November 2005.
  74. C. F. Hester and D. Casasent, "Multivariant technique for multiclass pattern recognition," Appl. Opt. 19, pp. 1758-1761 (1980).
  75. Mahalanobis, B. V. K. Vijaya Kumar, and D. Casasent, "Minimum average correlation energy filters," Appl. Opt. 26, pp. 3633-3630 (1987).
  76. M. Savvides, B. V. K. Vijaya Kumar and P. Khosla, "Face verification using correlation filters," Proc. Of Third IEEEAutomatic Identification Advanced Technologies, Tarrytown, NY, pp. 56-61 (2002)
  77. B. V. K. Vijaya Kumar, MariosSavvides, ChunyanXie, KrithikaVenkataramani, Jason Thornton, and AbhijitMahalanobis, "Biometric Verification with Correlation Filters," Appl. Opt. 43, 391-402 (2004)
  78. Savvides, M. Vijaya Kumar, B. V. K. ; Khosla, P. K. " Cancelable biometric filters for face recognition" Pattern Recognition, 2004. ICPR 2004Page(s): 922 - 925 Vol. 3
  79. ChunyanXie, B. V. K. Vijaya Kumar, S. Palanivel and B. Yegnanarayana, ' A Still-to-Video Face Verification System Using Advanced Correlation Filters ,' in International Conference on Biometric Authentication, pp. 102-108, 2004.
  80. Elias Rentzeperis,Thesis on A Comparative Analysis of Face RecognitionAlgorithms: Hidden Markov Models, Correlation Filters and Laplacianfaces Vs. Linear subspace projection and Elastic Bunch Graph Matching.
  81. T. F. Cootes, A. Hill, C. J. Taylor, J. Haslam, The use ofactive shape models for locating structures in medicalimages, IPMI, 1993, pp. 33–47.
  82. G. Hamarneh. Active shape models, modeling shape variations and graylevel information and an application to image search and classification. Technical report, The Imaging and Image Analysis Group, Departmentof Signals and Systems, Chalmers University of Technology, Gothenburg,Sweden, 1998.
  83. X. Hou, S. Li, H. Zhang, Direct appearance models,Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, pp. 828–833.
  84. K. I. Diamantaras and S. Y. Kung, Principal Component Neural Networks:Theory and Applications. New York: Wiley, 1996.
  85. M. J. Er, S. Wu, J. Lu, and H. L. Toh, "Face recognition with radialbasis function (RBF) neural networks," IEEE Trans. Neural Netw. , vol. 13, no. 3, pp. 697–710, May 2002.
  86. J. Lu, X. Yuan, and T. Yahagi, "A method of face recognition based on fuzzy c-means clustering and associated sub-NNs," IEEE Trans. Neural Netw. , vol. 18, no. 1, pp. 150–160, Jan. 2007.
  87. P. Melin, C. Felix, and O. Castillo, "Face recognition using modularneural networks and the fuzzy Sugeno integral for response integration," Int. J. Intell. Syst. , vol. 20, no. 2, pp. 275–291, Feb. 2005.
  88. Face Recognition Using Self-Organizing Maps, Qiu Chen, Koji Kotani, Feifei Lee and TadahiroOhmi, Tohoku University, Japan.
  89. Neagoe, V. -E. , 'Concurrent self-organizing maps for pattern classification' , Cognitive Informatics, 2002. Proceedings. First IEEE International Conference on 2002, Page(s): 304 - 312
  90. A. S. Raja and V. JosephRaj ,'neural network based supervised self organizing maps for face recognition', International Journal on Soft Computing (IJSC) Vol. 3, No. 3, August 2012
  91. Gregoire Lefebvre and Christophe Garcia, 'A probabilistic Self-Organizing Map for facial recognition ', 19th International Conference on Pattern Recognition, (ICPR) 8-11 Dec. 2008 Page(s): 1 - 4
  92. C. Liu and H. Wechsler, Probablistic reasoning models for face recognition, in Proc. of Computer Vision andPattern Recognition, 827-832, 1998.
  93. Moghaddam, B. , Nastar, C. , Pentland, A. : A Bayesian similarity measure for direct image matching. In: 13th International Conference on Pattern Recognition. (1996) II: 350–358.
  94. Wilman W. W. Zouand Pong C. Yuen, "Very Low Resolution Face Recognition Problem," IEEE transactions on image processing, vol. 21, no. 1, January 2012.
  95. N. Sudha, A. R. Mohan, Pramod K. Meher, "A Self-Configurable Systolic Architecture for Face Recognition System Based on Principal Component Neural Network," IEEE transactions on circuits and systems for video technology, vol. 21, no. 8, august 2011.
  96. Chengjun Liu and Harry Wechsler, "Independent Component Analysis of Gabor Features for Face Recognition,"IEEE transactions on neural networks, vol. 14, no. 4, july 2003.
  97. Bai-Ling Zhang, Haihong Zhang and Shuzhi Sam Ge, "Face Recognition by Applying Wavelet Subband Representation and Kernel Associative Memory,"IEEE transactions on neural networks, vol. 15, no. 1, january 2004.
  98. Wenchao Zhang, Shiguang Shan, Xilin Chen and Wen Gao, "Local Gabor Binary Patterns Based on Kullback–Leibler Divergence for Partially Occluded Face Recognition," IEEE signal processing letters, vol. 14, no. 11, november 2007.
  99. Zhiming Liu and Chengjun Liu, "A Hybrid Color and Frequency Features Method for Face Recognition," IEEE transactions on image processing, vol. 17, no. 10, october 2008.
  100. G. C. Feng and Pong C. Yuen, "Recognition of Head-&-Shoulder Face Image Using Virtual Frontal View Image" IEEE transactions on systems, man, and cybernetics—part a: cybernetics, vol. 30, no. 6, november 2000.
  101. Stan Z. Li,RuFeng Chu, ShengCai Liao, and Lun Zhang, "Illumination Invariant Face Recognition Using Near-Infrared Images" IEEE transactions on pattern analysis and machine intelligence, vol. 29, no. 4, april 2007.
  102. Richa Singh, MayankVatsa, ArunRoss,andAfzelNoore," A Mosaicing Scheme for Pose-Invariant Face Recognition" IEEE transactions on systems, man, and cybernetics—part b: cybernetics, vol. 37, no. 5, october 2007
  103. Dirk Smeets, Peter Claes, JeroenHermans, Dirk Vandermeulen, and Paul Suetens, "A Comparative Study of 3-D Face RecognitionUnder Expression Variations" IEEE transactions on systems, man, and cybernetics part c: applications and reviews, vol. 42, no. 5, september 2012.
  104. Zhifeng Li, Unsang Park, and Anil K. Jain "A Discriminative Model for Age Invariant Face Recognition"- IEEE transactions on information forensics and security, vol. 6, no. 3,september 2011.
  105. Kim K. I. , Jung K. and Kim H. J. 2002. Face recognition using kernel principal component analysis. IEEE Signal Processing Letters, Vol. 9, pp. 40–42.
  106. A. Nefian, "A Hidden Markov Model-Based Approach for Face Detection andRecognition", PhD dissertation in Electrical Engineering, Georgia Instituteof Technology
  107. Martinez, A. M. , Kak, A. C. , 2001. PCA versus LDA. IEEE Trans. PatternAnal. Machine Intell. 23 (2), 228–233.
  108. Bartlett Marian, Stewart, Movellan Javier, R. , Sejnowski Terrence, J. ,2002. Face recognition by independent component analysis. IEEE Trans. Neural Networks 13 (6), 1450–1464.
  109. Chirag Patel and RipalPatel,'Gaussian mixture model based Moving object detection from video sequence',International Conference and Workshop on Emerging Trends in Technology (ICWET 2011) – TCET, Mumbai, India
  110. Chirag I Patel and Sanjay Garg,' Robust Face Detection using Fusion of Haar and Daubechies Orthogonal Wavelet Template ' International Journal of Computer Applications (0975 – 8887) Volume 46– No. 6, May 2012 38
  111. Chirag. I. Patel and Ripal Patel, "Contour Based Object Tracking," International Journal of Computer and Electrical Engineering vol. 4, no. 4, pp. 525-528, 2012.
  112. Chirag. I. Patel and Ripal Patel, "Object Counting in Video Sequences," International Journal of Computer and Electrical Engineering vol. 4, no. 4, pp. 522-524, 2012.
  113. Chirag I. Patel, Ripal Patel and Palak Patel, 'Goal Detection from Unsupervised Video Surveillance',Advances in Computing and Information Technology Communications in Computer and Information Science Volume 198, 2011, pp 76-88
  114. Chirag Patel, Ripal Patel and Palak Patel . Handwritten Character Recognition using Neural Network, International Journal of Scientific and Engineering Research, Volume 2, Issue 4, April 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Still Face Recognition Video Face Recognition Biometric System