A Survey on Classical and Modern Face Recognition Techniques

Authors

  • M. ShalimaSulthana  Research Scholar, Department of Computer Science and Engineering, YSR Engineering College of YVU Yogivemana University-Kadapa, Andhra Pradesh, India
  • C. NagaRaju  Professor, Department of Computer Science and Engineering, YSR Engineering College of YVU, Yogivemana University-Kadapa, Andhra Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT21762

Keywords:

Abstract

During the previous few centuries, facial recognition systems have become a popular research topic. On account of its extraordinary success and vast social applications; it has attracted significant study attention from a wide range of disciplines in the last five years - including “computer-vision”, “artificial-intelligence”, and “machine-learning”. As with most face recognition systems, the fundamental goal involves recognizing a person's identity by means of images, video, data streams, and context information. As a result of our research; we've outlined some of the most important applications, difficulties, and trends in scientific and social domains. This research, the primary goal is to summarize modern facial recognition algorithms and to gain a general perceptive of how these techniques act on diverse datasets. Aside from that, we also explore some significant problems like illumination variation, position, aging, occlusion, cosmetics, scale, and background are some of the primary challenges we examine. In addition to traditional face recognition approaches, the most recent research topics such as sparse models, deep learning, and fuzzy set theory are examined in depth. There's also a quick discussion of basic techniques, as well as a more in-depth. As a final point, this research explores the future of facial recognition technologies and their possible importance in the emerging digital society.

References

  1. Poon G, Kwan KC, Pang W (2019) Occlusion-robust bimanual gesture recognition by fusing multi-views. Multimed Tools Appl 78 (16):23469–23488
  2. Wong Y, Chen S, Mau S, Sanderson C, Lovell BC (2011) Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition. In: IEEE Biometrics workshop, computer vision and pattern recognition (CVPR) workshops. IEEE, pp 81–88
  3. Ouyang D, Zhang Y, Shao J (2019) Video-based person re-identification via spatio-temporal attentional and two-stream fusion convolutional networks. Pattern Recogn Lett 117:153–160.
  4. VS Manjula, Lt Dr S Santhosh Baboo, et al. Face detection identification
  5. and tracking by prdit algorithm using image database for crime investigation. International JournalofComputerApplications,38(10):40–46,2012.
  6. Karen Lander, Vicki Bruce, and Markus Bindemann. Use-inspired basic research on individual differences in face identification: Implications for criminal investigation and security. Cognitive research: principles and implications,3(1):1–13,2018.
  7. ”Yongmei Hu, Heng An, Yubing Guo, Chunxiao Zhang, and Ye Li. The development status and prospects on the face recognition. In Bioinformatics and Biomedical Engineering (iCBBE), 2010 4th International Conference on, 2010.”
  8. Garain J, Kumar RK, Kisku DR, Sanyal G (2019) Addressing facial dynamics using k-medoids cohort selection algorithm for face recognition. Multimed Tools Appl 78(13):18443–18474.
  9. Mahmood A, Uzair M, Al-mȧadeed S (2018) Multi-order statistical descriptors for real-time face recognition and object classification. IEEE Access 6:12993–13004.
  10. Naoufel Werghi, Claudio Tortorici, Stefano Berretti and Alberto Del Bimbo, “Boosting 3D LBP-Based Face Recognition by Fusing Shape and Texture Descriptors on the Mesh”, IEEE Transactions on Information Forensics and Security, Vol. 11, Issue 5, pp. 964 – 979, Jan 2016
  11. Jiwen Lu , Venice Erin Liong and Jie Zhou, “Simultaneous Local Binary Feature Learning and Encoding for Homogeneous and Heterogeneous Face Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PP, Issue 99, pp.1-1, Aug 2017.
  12. Swati Manhotra and Reecha Sharma, “Facial Feature Extraction Using Local Binary Pattern and Local Ternary Pattern with Gradient Based Illumination Normalization”, Internaltional Journal of Advanced Research in Computer Science, Vol. 8, Issue 7, Aug 2017.
  13. Rajesh Kumar Gupta,Umesh Kumar Sahu ,” Real Time Face Recognition under Different Conditions “,International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 1, January 2013 ,86-89.
  14. C.Nagaraju, B.Srinu, E. Srinivasa Rao― An efficient Facial Features extraction Technique for Face Recognition system Using Local Binary Patterns‖ International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278 -3075, Volume-2, Issue-6, May 2013.
  15. Di Huang, Caifeng Shan, Mohsen Ardabilian, Yunhong Wang and Liming Chen―Local Binary Patterns and Its Application to Facial Image Analysis: A Survey―IEEE Transactions On Systems, man, and cybernetics—part c: applications and reviews, vol. 41, no. 6, november 2011.
  16. Huang, D.; Shan, C.; Ardabilian, M.; Wang, Y.; Chen, L. Local binary patterns and its application to facial image analysis: A survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 2011, 41, 765–781. 13.
  17. Belhumeur, P.N.; Hespanha, J.P.; Kriegman, D. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 1997, 19, 711–720.
  18. Liu, C.; Wechsler, H. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Process. 2002, 11, 467–476.
  19. Lowe, D.G. Distinctive image features from scale-invariant key points. Int. J. Comput. Vision 2004, 60, 91–110.
  20. Zhang, J.; Yan, Y.; Lades, M. Face recognition: Eigen face, elastic matching, and neural nets. Proc. IEEE 1997, 85, 1423–1435.
  21. Wright, J.; Yang, A.Y.; Ganesh, A.; Sastry, S.S.; Ma, Y. Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31, 210–227.
  22. Y. Pang, Y. Yuan and X. Li, Iterative subspace analysis based on feature line distance, Image Processing, IEEE Transactions on, vol.18, pp.903-907, 2009.
  23. S. Lawrence, C. Giles, A. C. Tsoi and A. Back, Face recognition: A convolutional neural-network approach, Neural Networks, IEEE Transactions on, vol.8, pp.98-113, 1997.
  24. S. Yan, H. Wang, X. Tang and T. Huang, Exploring feature descriptors for face recognition, Acoustics, Speech and Signal Processing, IEEE International Conference on, vol.1, pp.629-632, 2007.
  25. Q. Z. C. Zhou, X. Wei and B. Xiao, Image reconstruction for face recognition based on fast ICA , International Journal of Innovative Computing, Information and Control, vol.4, no.7, pp.1723-1732, 2008.
  26. J. Yang, A. Frangi, J.-Y. Yang, D. Zhang and Z. Jin, Kpca plus lda: A complete kernel fisher discriminant framework for feature extraction and recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, pp.230-244, 2005.
  27. C. Naga Raju, SivaPriya. T, prudvi.ch “A novel method for recognizing face to indicate the state of emotion in order to avoid consistent effect on decisions making” has been published in International Journal of Advancements in Computer Science & Information Technology (IJACSIT) September 2011Edition.pp.10-17.
  28. Kanade T (1973) Picture processing system by computer complex and recognition of human faces. Ph.D. thesis, Kyoto University, Japan.
  29. Sirovich L, Kirby M (1987) Low-dimensional procedure for the characterization of human faces. J Opt Soc Am A 4(3):519–524
  30. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86
  31. Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. In: International conference on audio- and video-based biometric person authentication, pp 125–142.
  32. Guo G, Li S, Chan KL (2000) Face recognition by support vector machines. In: 4Th IEEE international conference on automatic face and gesture recognition (FG 2000), Grenoble, pp 196–201
  33. Zhao W, Chellappa R, Phillips PJ (1999) Subspace linear discriminant analysis for face recognition. Citeseer
  34. Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 13(6):1450–1464.
  35. Rajkiran Gottumukkal and Vijayan K Asari. An improved face recognition technique based on modular pca approach. Pattern Recognition Letters, 25(4):429–436, 2004.
  36. D., C., Hoyle, M., and Rattray. Pca learning for sparse high-dimensional data. Epl, 2003.
  37. K. Vijay and K. Selvakumar. Brain fmri clustering using interaction k-means algorithm with pca. In 2015 International Conference on Communications and Signal Processing (ICCSP), 2015
  38. Jianke Li, Baojun Zhao, Zhang Hui, and Jichao Jiao. Face recognition system using svm classifier and feature extraction by pca and lda combination. In Computational Intelligence and Software Engineering, 2009.
  39. CiSE 2009. International Conference on, 2010.
  40. Frank Vogt, Boris Mizaikoff, and Maurus Tacke. Numerical methods for accelerating the pca of large data sets applied to hyperspectral imaging. InEnvironmental & Industrial Sensing, 2002.
  41. Carlos Ordonez, Naveen Mohanam, and Carlos Garcia-Alvarado. Pca for large data sets with parallel data summarization. Distributed & Parallel Databases, 32(3):377–403, 2014.
  42. Shireesha Chintalapati and MV Raghunadh. Automated attendance management system based on face recognition algorithms. In 2013 IEEE International Conference on Computational Intelligence and Computing Research, pages 1–5. IEEE, 2013.
  43. Juwei Lu, Kostantinos N. Plataniotis, and Anastasios N. Venetsanopoulos. Face recognition using lda-based algorithms. IEEE Transactions on Neural Networks, 14(1):195–200, 2003.
  44. Jianke Li, Baojun Zhao, Zhang Hui, and Jichao Jiao. Face recognition system using svm classifier and feature extraction by pca and lda combination. In Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on, 2010.
  45. M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cognitive Neurosci., vol. 3, no. 1, pp. 71–86, 1991.
  46. G. Cottrell and J. Metcalfe, “Face, gender and emotion recognition using holons,” in Advances in Neural Information Processing Systems, D. Touretzky, Ed. San Mateo, CA: Morgan Kaufmann, 1991, vol. 3, pp. 564–571
  47. P. S. Penev and J. J. Atick, “Local feature analysis: A general statistical theory for object representation,” Network: Comput. Neural Syst., vol. 7, no. 3, pp. 477–500, 1996
  48. P. Comon, “Independent component analysis—A new concept?,” Signal Processing, vol. 36, pp. 287–314, 1994
  49. A. Pentland, B. Moghaddam, and T. Starner, “View-based and modular eigenspaces for face recognition,” in Proc. IEEE Conf. Comput. Vision Pattern Recognition, 1994, pp. 84–91.
  50. M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cognitive Neurosci., vol. 3, no. 1, pp. 71–86, 1991.
  51. A. J. Bell and T. J. Sejnowski, “An information-maximization approach to blind separation and blind deconvolution,” Neural Comput., vol. 7, no. 6, pp. 1129–1159, 1995.
  52. A. Cichocki, R. Unbehauen, and E. Rummert, “Robust learning algorithm for blind separation of signals,” Electron. Lett., vol. 30, no. 7, pp. 1386–1387, 1994.
  53. P. Comon, “Independent component analysis—A new concept?,” Signal Processing, vol. 36, pp. 287–314, 1994.
  54. S. Laughlin, “A simple coding procedure enhances a neuron’s information capacity,” Z. Naturforsch., vol. 36, pp. 910–912, 1981.
  55. Cortescorinna and Vapnikvladimir. Support-vector networks. Machine Learning, 1995.
  56. Aixin Sun, Ee-Peng Lim, and Ying Liu. On strategies for imbalanced text classification using svm: A comparative study. Decision Support Systems, 48(1):191–201, 2009.
  57. Ahonen T, Hadid A, Pietikȧinen M (2004) Face recognition with local binary patterns. In: Proceedings of 8th European Conference on Computer Vision-ECCV, Prague, Czech Republic, Part I, pp 469–481.
  58. Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650Return to ref 239 in article.
  59. J Krizaj, V. Struc, N Pavesi, “Adaptation of SIFT Features for Face Recognition under Varying Illumination” , MIPRO 2010, May 24-28, 2010, Opatija, Croatia.
  60. Haeseong Lee1, Semi Jeon, Inhye Yoon, and Joonki Paik1, “Recent Advances in Feature Detectors and Descriptors: A Survey”, IEIE Transactions on Smart Processing and Computing, vol. 5, no. 3, June 2016http://dx.doi.org/10.5573/IEIESPC.2016.5.3.153.
  61. Lavanya B, Inbarani HH (2018) A novel hybrid approach based on principal component analysis and tolerance rough similarity for face identification. Neural Comput Appl 29(8):289–299.
  62. Hashemi VH, Gharahbagh AA (2015) Article:a novel hybrid method for face recognition based on 2d wavelet and singular value decomposition. Amer J Netw Commun 4(4):90–94.
  63. Kirby M, Sirovich L (1990) Application of the KarhumenLoeve procedure for the characterization of human faces. IEEE Transactions on Pattern Analysis Machine Intelligence 12(1):103–108
  64. Solunke V, Kudle P, Bhise A, Naik A, Prasad JR (2014) A comparison between feature extraction techniques for face recognition. International Journal of Emerging Research in Management & Technology 3:38–41.
  65. Pawlak Z (1982) Rough sets. Int J Parallel Prog 11(5):341–356 Pawlak Z (2002) Rough set theory and its applications. Journal of Telecommunications and information technology:7–10.
  66. Swiniarski, R. (2000). An application of rough sets and Haar wavelets to face recognition. In International Conference on Rough Sets and Current Trends in Computing. Springer Berlin Heidelberg. pp. 561–568.
  67. Kim D (2001) Data classification based on tolerant rough set. Pattern Recogn 34(8):1613–1624
  68. Kim D, Bang SY (2000) A handwritten numeral character classification using tolerant rough set. IEEE Trans Pattern Anal Mach Intell 22(9):923–937.
  69. Kwak KC, Pedrycz W (2005) Face recognition using a fuzzy fisherface classifier. Pattern Recogn 38(10):1717–1732.
  70. Li X (2014) Face recognition method based on fuzzy 2dpca. J Electr Comput Eng 2014:919041:1–919041:7
  71. Oulefki A, Mustapha A, Boutellaa E, Bengherabi M, Tifarine AA (2018) Fuzzy reasoning model to improve face illumination invariance. SIViP 12 (3):421–428
  72. Huang P, Gao G, Qian C, Yang G, Yang Z (2017) Fuzzy linear regression discriminant projection for face recognition. IEEE Access 5:4340–4349
  73. Campomanes-Alvarez C, Ȧlvarez BRC, Guadarrama S, Ibȧṅez Ȯ, Cordȯn O (2017) An experimental study on fuzzy distances for skull-face overlay in craniofacial superimposition. Fuzzy Sets Syst 318:100–119
  74. Arashloo SR (2016) A comparison of deep multilayer networks and markov random field matching models for face recognition in the wild. IET Comput Vis 10(6):466–474.
  75. Sun Y, Wang X, Tang X (2013) Hybrid deep learning for face verification. In: IEEE International conference on computer vision, ICCV, Sydney, pp 1489–1496.
  76. Stonham TJ (1986) Practical Face Recognition and Verification with Wisard. Springer, Netherlands, pp 426–441Return to ref 230 in article
  77. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv (CSUR) 35(4):399–458Return to ref 298 in article
  78. Arashloo SR (2016) A comparison of deep multilayer networks and markov random field matching models for face recognition in the wild. IET Comput Vis 10(6):466–474Return to ref 15 in article
  79. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554.
  80. Sun Y, Wang X, Tang X (2014) Deep learning face representation from predicting 10, 000 classes. In: IEEE Conference on computer vision and pattern recognition, CVPR, Columbus, pp 1891–1898.
  81. Sun Y, Chen Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification. In: Proceedings of the 27th International Conference on Neural Information Processing Systems – Vo.lume 2, NIPS, pp 1988–1996
  82. Sun Y, Liang D, Wang X, Tang X (2015) Deepid3: Face recognition with very deep neural networks. CoRR arXiv:1502.00873
  83. Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90.
  84. Chen Y, Patel VM, Phillips PJ, Chellappa R (2012) Dictionary-based face recognition from video. In: Proceedings of 12th European Conference on Computer Vision ECCV, Florence, Part VI, pp 766–779.

Downloads

Published

2021-12-30

Issue

Section

Research Articles

How to Cite

[1]
M. ShalimaSulthana, C. NagaRaju, " A Survey on Classical and Modern Face Recognition Techniques , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 6, pp.57-79, November-December-2021. Available at doi : https://doi.org/10.32628/CSEIT21762