Classical 2D Face Recognition: A Survey on Methods, Face Databases, and Performance Evaluation

  • Manoj Kumar Naik
  • Aneesh WunnavaEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 109)


The visual system is the ultimate model for computer vision systems. Face recognition is one of the essential biometric-based methods of computer vision from the perspective of safety and security. The research in face recognition has improved significantly during the way back 1970 to present based on the various classification technique. This paper presents a survey of some most significant classical 2D classification techniques in face recognition, the well-known face databases for evaluation of methods, and performance evaluation techniques.


Face recognition 2D face recognition Face databases 


  1. 1.
    Chellappa R, Wilson CL, Sirohey S (1995) Human and machine recognition of faces: a survey. Proc IEEE 83:705–740CrossRefGoogle Scholar
  2. 2.
    Perronnin F, Dugelay J-L (2003) An introduction to biometrics and face recognition. In: IMAGE’2003: learning, understanding, information retrievalGoogle Scholar
  3. 3.
    Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35:399–458CrossRefGoogle Scholar
  4. 4.
    Kong SG, Heo J, Abidi BR, Paik J, Abidi MA (2005) Recent advances in visual and infrared face recognition - a review. Comput Vis Image Underst 97:103–135CrossRefGoogle Scholar
  5. 5.
    Abate AF, Nappi M, Riccio D, Sabatino G (2007) 2D and 3D face recognition: A survey. Pattern Recogn Lett 28:1885–1906CrossRefGoogle Scholar
  6. 6.
    Beham MP, Roomi SMM (2013) A review of face recognition methods. Int J Pattern Recogn Artif Intell 27:1356005CrossRefGoogle Scholar
  7. 7.
    Brunelli R, Poggio T (1993) Face recognition: features versus templates. IEEE Trans Pattern Anal Mach Intell 15:1042–1052CrossRefGoogle Scholar
  8. 8.
    Beyer K, Goldstein J, Ramakrishnan R, Shaft U (1999) When Is “Nearest Neighbor” Meaningful? In: Beeri C, Buneman P (eds) Database Theory — ICDT’99, vol 1540. Springer, Berlin, Heidelberg, pp 217–235CrossRefGoogle Scholar
  9. 9.
    Sirovich L, Kirby M (1987) Low-dimensional procedure for the characterization of human faces. J Opt Soc Am A 4:519–524CrossRefGoogle Scholar
  10. 10.
    Bruce V (1999) Identification of human faces. In: 1999 Seventh international conference on (Conference Publication No. 465) image processing and its applications, vol 612, pp 615–619Google Scholar
  11. 11.
    Goldstein AJ, Harmon LD, Lesk AB (1971) Identification of human faces. Proc IEEE 59:748–760CrossRefGoogle Scholar
  12. 12.
    Jolliffe IT (1986) Principal cornponent analysis. Springer, New YorkCrossRefGoogle Scholar
  13. 13.
    Karhunen K (1946) Uber lineare methoden in der wahrscheinlichkeits-rechnun. Ann Acad Sri Fennicae ser A1 Math Phys 37Google Scholar
  14. 14.
    Fukunaga K (1972) lntroduction to statistical pattern recognition. Academic, New YorkGoogle Scholar
  15. 15.
    Fukunaga K, Koontz WLZ (1970) Application of the Karhunen Loeve expansion to feature selection and ordering. IEEE Trans Comput C-19:311–318zbMATHCrossRefGoogle Scholar
  16. 16.
    Pearson K (1901) On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2\Google Scholar
  17. 17.
    Hotelling H (1933) Analysis of a complex of statistical variables into principal component. J Educ Psychol 24 (1933)zbMATHCrossRefGoogle Scholar
  18. 18.
    Gonzalez RC, Wintz PA (1987) Digital image processing. Addison-Wesley, Reading, MAzbMATHGoogle Scholar
  19. 19.
    Watanabe S (1965) Karhunen-Loeve expansion and factor analysis theoretical remarks and applications. In: 4th Prague Conference Information TheoryGoogle Scholar
  20. 20.
    Kirby M, Sirovich L (1990) Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Pattern Anal Mach Intell 12:103–108CrossRefGoogle Scholar
  21. 21.
    Turk MA, Pentland AP (1991) Face recognition using eigenfaces. In: Proceedings CVPR’91, IEEE computer society conference on computer vision and pattern recognition, pp 586–591Google Scholar
  22. 22.
    Turk MA, Pentland AP (1991) Eigenfaces for recognition. J Cogn Neurosci 3:71–86CrossRefGoogle Scholar
  23. 23.
    Moon H, Phillips PJ (2001) Computational and performance aspects of PCA-based face recognition algorithms. Perception 30:303–321CrossRefGoogle Scholar
  24. 24.
    Yambor WS, Draper BA, Beveridge JR (2000) Analyzing PCA-based face recognition algorithms: eigenvector selection and distance measuresGoogle Scholar
  25. 25.
    Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. J Opt Soc Am A 14:1724–1733CrossRefGoogle Scholar
  26. 26.
    Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugenics 7:179–188CrossRefGoogle Scholar
  27. 27.
    Duchene J, Leclercq S (1988) An optimal transformation for discriminant and principal component analysis. IEEE Trans Pattern Anal Mach Intell 10:978–983zbMATHCrossRefGoogle Scholar
  28. 28.
    Belhumeur PN, Hespanha JP, Kriegman D (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720CrossRefGoogle Scholar
  29. 29.
    Chen L-F, Liao H-YM, Ko M-T, Lin J-C, Yu G-J (2000) A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recogn 33:1713–1726CrossRefGoogle Scholar
  30. 30.
    Martinez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23:228–233CrossRefGoogle Scholar
  31. 31.
    Yu H, Yang J (2001) A direct LDA algorithm for high-dimensional data with application to face recognition. Patttern Recogn 34:2067–2070zbMATHCrossRefGoogle Scholar
  32. 32.
    Kostantinos JL, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using LDA-based algorithms. IEEE Trans Neural Networks 14:195–200CrossRefGoogle Scholar
  33. 33.
    Yang J, Yang J-Y (2003) Why can LDA be performed in PCA transformed space? Pattern Recogn 36:563–566CrossRefGoogle Scholar
  34. 34.
    Schlkopf B, Smola A, Muller K-R (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10:1299–1319CrossRefGoogle Scholar
  35. 35.
    Yang J, Yang J-Y, Frangi AF (2003) Combined fisherfaces framework. Image Vis Comput 21:1037–1044CrossRefGoogle Scholar
  36. 36.
    Xiao-Yuan J, Zhang D, Yuan-Yan T (2004) An improved LDA approach. IEEE Trans Syst Man Cybern Part B Cybern 34:1942–1951CrossRefGoogle Scholar
  37. 37.
    Lu J, Plataniotis KN, Venetsanopoulos AN (2005) Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition. Pattern Recogn Lett 26:181–191CrossRefGoogle Scholar
  38. 38.
    Zhao M, Zhang Z, Chow TWS, Li B (2014) Soft label based linear discriminant analysis for image recognition and retrieval. Comput Vis Image Underst 121:86–99CrossRefGoogle Scholar
  39. 39.
    Ye J (2005) Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. J Mach Learn Res 6:483–502MathSciNetzbMATHGoogle Scholar
  40. 40.
    Zheng W-S, Lai JH, Yuen PC, Li SZ (2009) Perturbation LDA: learning the difference between the class empirical mean and its expectation. Pattern Recogn 42:764–779zbMATHCrossRefGoogle Scholar
  41. 41.
    Jutten C, Herault J (1991) Blind separation of sources, part 1: an adaptive algorithm based on neuromimetic architecture. Sign Proces 24:1–10zbMATHCrossRefGoogle Scholar
  42. 42.
    Comon P (1994) Independent component analysis, a new concept? Sign Proces 36:287–314zbMATHCrossRefGoogle Scholar
  43. 43.
    Keun-Chang K, Pedrycz W (2007) Face recognition using an enhanced independent component analysis approach. IEEE Trans Neural Networks 18:530–541CrossRefGoogle Scholar
  44. 44.
    Rabiner L, Huang B (1993) Fundamentals of speech recognition. Prentice-Hal, Englewood Cliffs, NJGoogle Scholar
  45. 45.
    Baum LE, Petrie T (1966) Statistical inference for probabilistic functions of finite state Markov chains. Ann Math Stat 37:1554–1563MathSciNetzbMATHCrossRefGoogle Scholar
  46. 46.
    Baum LE, Eagon JA (1967) An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology, pp 360–363MathSciNetzbMATHCrossRefGoogle Scholar
  47. 47.
    Baum LE, Petrie T, Soules G, Weiss N (1970) A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann Math Stat 41:164–171MathSciNetzbMATHCrossRefGoogle Scholar
  48. 48.
    Baum LE (1972) An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes. In: Inequalities III, Academic Press pp 1–8Google Scholar
  49. 49.
    Samaria F, Young S (1994) HMM-based architecture for face identification. Image Vis Comput 12:537–543CrossRefGoogle Scholar
  50. 50.
    Nefian AV, Hayes MH, III (1998) Hidden Markov models for face recognition. In: Proceedings of the 1998 IEEE international conference on acoustics, speech and signal processing, vol 2725, pp 2721–2724Google Scholar
  51. 51.
    Nefian AV, Hayes MH, III (1998) Face detection and recognition using hidden Markov models. In: Proceedings 1998 International Conference on Image Processing, ICIP 98, vol 141, pp 141–145Google Scholar
  52. 52.
    Wiskott L, Fellous JM, Kuiger N, von der Malsburg C (1997) Face recognition by elastic bunch graph matching. IEEE Trans Pattern Anal Mach Intell 19:775–779CrossRefGoogle Scholar
  53. 53.
    Daugman JG (1988) Complete discrete 2D gabor transform by neural networks for image analysis and compression. IEEE Trans Acoustics Speech Signal Proces 36:1169–1179zbMATHCrossRefGoogle Scholar
  54. 54.
    Kela N, Rattani A, Gupta P (2006) Illumination invariant elastic bunch graph matching for efficient face recognition. In: CVPRW’06 Conference on Computer Vision and Pattern Recognition Workshop, pp 42–42Google Scholar
  55. 55.
    Pervaiz AZ (2010) Real time face recognition system based on EBGM framework. In: 2010 12th International conference on computer modelling and simulation (UKSim), pp 262–266Google Scholar
  56. 56.
    Hanmandlu M, Gupta D, Vasikarla S (2013) Face recognition using elastic bunch graph matching. In: 2013 IEEE applied imagery pattern recognition workshop: sensing for control and augmentation, pp 1–7Google Scholar
  57. 57.
    Wang Y, Wu Y (2010) Face recognition using Intrinsicfaces. Pattern Recogn 43:3580–3590zbMATHCrossRefGoogle Scholar
  58. 58.
    Chengjun L, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Proc 11:467–476CrossRefGoogle Scholar
  59. 59.
    Yang M-H (2002) Kernel Eigenfaces vs. kernel fisherfaces: face recognition using kernel methods. In Proceedings of the fifth IEEE international conference on automatic face and gesture recognition, IEEE computer society, p 215Google Scholar
  60. 60.
    Lu J, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans on Neural Networks 14:117–126CrossRefGoogle Scholar
  61. 61.
    He X, Shuicheng Y, Yuxiao H, Niyogi P, Hong-Jiang Z (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27:328–340CrossRefGoogle Scholar
  62. 62.
    He X, Niyogi P (2003) Locality preserving projections. In: advances in neural information processing systemsGoogle Scholar
  63. 63.
    Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28:2037–2041zbMATHCrossRefGoogle Scholar
  64. 64.
    Saul LK, Roweis ST (2003) Think globally, fit locally: unsupervised learning of low dimensional manifolds. J Mach Learn Res 4:119–155MathSciNetzbMATHGoogle Scholar
  65. 65.
    Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326CrossRefGoogle Scholar
  66. 66.
    Ridder DD, Duin RPW (2002) Locally linear embedding for classification. Delft University of TechnologyGoogle Scholar
  67. 67.
    Samko O, Marshall AD, Rosin PL (2006) Selection of the optimal parameter value for the Isomap algorithm. Pattern Recogn Lett 27:968–979CrossRefGoogle Scholar
  68. 68.
    Cai D, He X, Zhou C, Han J, Bao H (2007) Locality sensitive discriminant analysis. In: Proceedings of the 20th international joint conference on Artifical intelligence, Morgan Kaufmann Publishers Inc., Hyderabad, India, pp 708–713Google Scholar
  69. 69.
  70. 70.
    Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. In: Proceedings of the second IEEE workshop on applications of computer vision, pp 138–142Google Scholar
  71. 71.
    Martinez AM, Benavente R (1998) The ar face databaseGoogle Scholar
  72. 72.
    Li B, Li J, Tang K, Yao X (2015) Many-objective evolutionary algorithms: a survey. ACM Comput Surv 48:1–35CrossRefGoogle Scholar
  73. 73.
    Phillips PJ, Hyeonjoon M, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22:1090–1104CrossRefGoogle Scholar
  74. 74.
    Vázquez D, Fernández-Torres MJ, Ruiz-Femenia R, Jiménez L, Caballero JA (2018) MILP method for objective reduction in multi-objective optimization. Comput Chem Eng 108:382–394CrossRefGoogle Scholar
  75. 75.
    Phillips PJ, Wechsler H, Huang J, Rauss PJ (1998) The FERET database and evaluation procedure for face-recognition algorithms. Image Vis Comput 16:295–306CrossRefGoogle Scholar
  76. 76.
    Rizvi SA, Phillips PJ, Hyeonjoon M (1998) The FERET verification testing protocol for face recognition algorithms. In: Proceedings third IEEE international conference on automatic face and gesture recognition, pp 48–53Google Scholar
  77. 77.
  78. 78.
    Wang H, Jin Y, Yao X (2017) Diversity assessment in many-objective optimization. IEEE Trans Cybern 47:1510–1522CrossRefGoogle Scholar
  79. 79.
    Sim T, Baker S, Bsat M (2002) The CMU pose, illumination, and expression (PIE) database. In: Proceedings of fifth IEEE international conference on automatic face and gesture recognition, pp 46–51Google Scholar
  80. 80.
    Sim T, Baker S, Bsat M (2003) The CMU pose, illumination, and expression database. IEEE Trans Pattern Anal Mach Intell 25:1615–1618CrossRefGoogle Scholar
  81. 81.
    Sedarous S, El-Gokhy SM, Sallam E (2017) Multi-swarm multi-objective optimization based on a hybrid strategy. Alexandria Eng J 57(3):1619–1629CrossRefGoogle Scholar
  82. 82.
    Huang GB, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environmentsGoogle Scholar
  83. 83.
    Meza J, Espitia H, Montenegro C, Giménez E, González-Crespo R (2017) MOVPSO: vortex multi-objective particle swarm optimization. Appl Soft Comput 52:1042–1057CrossRefGoogle Scholar
  84. 84.
    Singh R, Vatsa M, Bhatt HS, Bharadwaj S, Noore A, Nooreyezdan SS (2010) Plastic surgery: a new dimension to face recognition. IEEE Trans Inf Forensics Secur 5:441–448CrossRefGoogle Scholar
  85. 85.
  86. 86.
    Hasan MK, Pal CJ (2011) Improving alignment of faces for recognition. In: 2011 IEEE international symposium on Robotic and Sensors Environments (ROSE), pp 249–254Google Scholar
  87. 87.
    Hasan MK, Pal C (2014) Experiments on visual information extraction with the faces of wikipedia. In: AAAI 2014Google Scholar
  88. 88.
    Wolf L, Hassner T, Taigman Y (2011) Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE Trans Pattern Anal Mach Intell 33:1978–1990CrossRefGoogle Scholar
  89. 89.
    Dantcheva A, Cunjian C, Ross A (2012) Can facial cosmetics affect the matching accuracy of face recognition systems? In: 2012 IEEE fifth international conference on Biometrics: Theory, Applications and Systems (BTAS), pp 391–398Google Scholar
  90. 90.
    Bainbridge WA, Isola P, Oliva A (2013) The intrinsic memorability of face photographs. J Exp Psychol Gen 142:1323–1334CrossRefGoogle Scholar
  91. 91.
    Chen C, Dantcheva A, Ross A (2013) Automatic facial makeup detection with application in face recognition. In: 2013 International Conference on IEEE Biometrics (ICB), pp 1–8 (2013)Google Scholar
  92. 92.
    Setty S, Husain M, Beham, P, Gudavalli J, Kandasamy M, Vaddi R, Hemadri V, Karure JC, Raju R, Rajan B, Kumar V, Jawahar CV (2013) Indian movie face database: a benchmark for face recognition under wide variations. In: 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp 1–5Google Scholar
  93. 93.
    Hong-Wei N, Winkler S (2014) A data-driven approach to cleaning large face datasets. In: 2014 IEEE International Conference on Image Processing (ICIP), pp 343–347Google Scholar
  94. 94.
    Vieira T, Bottino A, Laurentini A, De Simone M (2014) Detecting siblings in image pairs. Vis Comput 30:1333–1345CrossRefGoogle Scholar
  95. 95.
    Grother P, Micheals R, Phillips PJ (2002) Face recognition vendor test performance metrics. In: Kittler J, Nixon M (eds) Audio- and video-based biometric person authentication, vol 2688. Springer, Berlin Heidelberg, pp 937–945Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of ECEITER, Siksha O AnusandhanBhubaneswarIndia

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