Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Advances in Intelligent Computing and Communication

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 109))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Chellappa R, Wilson CL, Sirohey S (1995) Human and machine recognition of faces: a survey. Proc IEEE 83:705–740

    Article  Google Scholar 

  2. Perronnin F, Dugelay J-L (2003) An introduction to biometrics and face recognition. In: IMAGE’2003: learning, understanding, information retrieval

    Google Scholar 

  3. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35:399–458

    Article  Google Scholar 

  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 97:103–135

    Article  Google Scholar 

  5. Abate AF, Nappi M, Riccio D, Sabatino G (2007) 2D and 3D face recognition: A survey. Pattern Recogn Lett 28:1885–1906

    Article  Google Scholar 

  6. Beham MP, Roomi SMM (2013) A review of face recognition methods. Int J Pattern Recogn Artif Intell 27:1356005

    Article  Google Scholar 

  7. Brunelli R, Poggio T (1993) Face recognition: features versus templates. IEEE Trans Pattern Anal Mach Intell 15:1042–1052

    Article  Google Scholar 

  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 217–235

    Chapter  Google Scholar 

  9. Sirovich L, Kirby M (1987) Low-dimensional procedure for the characterization of human faces. J Opt Soc Am A 4:519–524

    Article  Google Scholar 

  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 615–619

    Google Scholar 

  11. Goldstein AJ, Harmon LD, Lesk AB (1971) Identification of human faces. Proc IEEE 59:748–760

    Article  Google Scholar 

  12. Jolliffe IT (1986) Principal cornponent analysis. Springer, New York

    Book  Google Scholar 

  13. Karhunen K (1946) Uber lineare methoden in der wahrscheinlichkeits-rechnun. Ann Acad Sri Fennicae ser A1 Math Phys 37

    Google Scholar 

  14. Fukunaga K (1972) lntroduction to statistical pattern recognition. Academic, New York

    Google Scholar 

  15. Fukunaga K, Koontz WLZ (1970) Application of the Karhunen Loeve expansion to feature selection and ordering. IEEE Trans Comput C-19:311–318

    Article  MATH  Google Scholar 

  16. Pearson K (1901) On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2\

    Google Scholar 

  17. Hotelling H (1933) Analysis of a complex of statistical variables into principal component. J Educ Psychol 24 (1933)

    Article  MATH  Google Scholar 

  18. Gonzalez RC, Wintz PA (1987) Digital image processing. Addison-Wesley, Reading, MA

    MATH  Google Scholar 

  19. Watanabe S (1965) Karhunen-Loeve expansion and factor analysis theoretical remarks and applications. In: 4th Prague Conference Information Theory

    Google Scholar 

  20. Kirby M, Sirovich L (1990) Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Pattern Anal Mach Intell 12:103–108

    Article  Google Scholar 

  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 586–591

    Google Scholar 

  22. Turk MA, Pentland AP (1991) Eigenfaces for recognition. J Cogn Neurosci 3:71–86

    Article  Google Scholar 

  23. Moon H, Phillips PJ (2001) Computational and performance aspects of PCA-based face recognition algorithms. Perception 30:303–321

    Article  Google Scholar 

  24. Yambor WS, Draper BA, Beveridge JR (2000) Analyzing PCA-based face recognition algorithms: eigenvector selection and distance measures

    Google Scholar 

  25. Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. J Opt Soc Am A 14:1724–1733

    Article  Google Scholar 

  26. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugenics 7:179–188

    Article  Google Scholar 

  27. Duchene J, Leclercq S (1988) An optimal transformation for discriminant and principal component analysis. IEEE Trans Pattern Anal Mach Intell 10:978–983

    Article  MATH  Google Scholar 

  28. Belhumeur PN, Hespanha JP, Kriegman D (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720

    Article  Google Scholar 

  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 33:1713–1726

    Article  Google Scholar 

  30. Martinez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23:228–233

    Article  Google Scholar 

  31. Yu H, Yang J (2001) A direct LDA algorithm for high-dimensional data with application to face recognition. Patttern Recogn 34:2067–2070

    Article  MATH  Google Scholar 

  32. Kostantinos JL, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using LDA-based algorithms. IEEE Trans Neural Networks 14:195–200

    Article  Google Scholar 

  33. Yang J, Yang J-Y (2003) Why can LDA be performed in PCA transformed space? Pattern Recogn 36:563–566

    Article  Google Scholar 

  34. Schlkopf B, Smola A, Muller K-R (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10:1299–1319

    Article  Google Scholar 

  35. Yang J, Yang J-Y, Frangi AF (2003) Combined fisherfaces framework. Image Vis Comput 21:1037–1044

    Article  Google Scholar 

  36. Xiao-Yuan J, Zhang D, Yuan-Yan T (2004) An improved LDA approach. IEEE Trans Syst Man Cybern Part B Cybern 34:1942–1951

    Article  Google Scholar 

  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 26:181–191

    Article  Google Scholar 

  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 121:86–99

    Article  Google Scholar 

  39. Ye J (2005) Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. J Mach Learn Res 6:483–502

    MathSciNet  MATH  Google Scholar 

  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 42:764–779

    Article  MATH  Google Scholar 

  41. Jutten C, Herault J (1991) Blind separation of sources, part 1: an adaptive algorithm based on neuromimetic architecture. Sign Proces 24:1–10

    Article  MATH  Google Scholar 

  42. Comon P (1994) Independent component analysis, a new concept? Sign Proces 36:287–314

    Article  MATH  Google Scholar 

  43. Keun-Chang K, Pedrycz W (2007) Face recognition using an enhanced independent component analysis approach. IEEE Trans Neural Networks 18:530–541

    Article  Google Scholar 

  44. Rabiner L, Huang B (1993) Fundamentals of speech recognition. Prentice-Hal, Englewood Cliffs, NJ

    Google Scholar 

  45. Baum LE, Petrie T (1966) Statistical inference for probabilistic functions of finite state Markov chains. Ann Math Stat 37:1554–1563

    Article  MathSciNet  MATH  Google Scholar 

  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 360–363

    Article  MathSciNet  MATH  Google Scholar 

  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 41:164–171

    Article  MathSciNet  MATH  Google Scholar 

  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 1–8

    Google Scholar 

  49. Samaria F, Young S (1994) HMM-based architecture for face identification. Image Vis Comput 12:537–543

    Article  Google Scholar 

  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 2721–2724

    Google Scholar 

  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 141–145

    Google Scholar 

  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 19:775–779

    Article  Google Scholar 

  53. Daugman JG (1988) Complete discrete 2D gabor transform by neural networks for image analysis and compression. IEEE Trans Acoustics Speech Signal Proces 36:1169–1179

    Article  MATH  Google Scholar 

  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 42–42

    Google Scholar 

  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 262–266

    Google Scholar 

  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 1–7

    Google Scholar 

  57. Wang Y, Wu Y (2010) Face recognition using Intrinsicfaces. Pattern Recogn 43:3580–3590

    Article  MATH  Google Scholar 

  58. Chengjun L, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Proc 11:467–476

    Article  Google Scholar 

  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 215

    Google Scholar 

  60. Lu J, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans on Neural Networks 14:117–126

    Article  Google Scholar 

  61. He X, Shuicheng Y, Yuxiao H, Niyogi P, Hong-Jiang Z (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27:328–340

    Article  Google Scholar 

  62. He X, Niyogi P (2003) Locality preserving projections. In: advances in neural information processing systems

    Google Scholar 

  63. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28:2037–2041

    Article  MATH  Google Scholar 

  64. Saul LK, Roweis ST (2003) Think globally, fit locally: unsupervised learning of low dimensional manifolds. J Mach Learn Res 4:119–155

    MathSciNet  MATH  Google Scholar 

  65. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326

    Article  Google Scholar 

  66. Ridder DD, Duin RPW (2002) Locally linear embedding for classification. Delft University of Technology

    Google Scholar 

  67. Samko O, Marshall AD, Rosin PL (2006) Selection of the optimal parameter value for the Isomap algorithm. Pattern Recogn Lett 27:968–979

    Article  Google Scholar 

  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 708–713

    Google Scholar 

  69. http://www.cam-orl.co.uk/facedatabase.html

  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 138–142

    Google Scholar 

  71. Martinez AM, Benavente R (1998) The ar face database

    Google Scholar 

  72. Li B, Li J, Tang K, Yao X (2015) Many-objective evolutionary algorithms: a survey. ACM Comput Surv 48:1–35

    Article  Google Scholar 

  73. Phillips PJ, Hyeonjoon M, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22:1090–1104

    Article  Google Scholar 

  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 108:382–394

    Article  Google Scholar 

  75. Phillips PJ, Wechsler H, Huang J, Rauss PJ (1998) The FERET database and evaluation procedure for face-recognition algorithms. Image Vis Comput 16:295–306

    Article  Google Scholar 

  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 48–53

    Google Scholar 

  77. http://cvc.yale.edu/projects/yalefaces/yalefaces.html

  78. Wang H, Jin Y, Yao X (2017) Diversity assessment in many-objective optimization. IEEE Trans Cybern 47:1510–1522

    Article  Google Scholar 

  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 46–51

    Google Scholar 

  80. Sim T, Baker S, Bsat M (2003) The CMU pose, illumination, and expression database. IEEE Trans Pattern Anal Mach Intell 25:1615–1618

    Article  Google Scholar 

  81. Sedarous S, El-Gokhy SM, Sallam E (2017) Multi-swarm multi-objective optimization based on a hybrid strategy. Alexandria Eng J 57(3):1619–1629

    Article  Google Scholar 

  82. Huang GB, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments

    Google Scholar 

  83. Meza J, Espitia H, Montenegro C, Giménez E, González-Crespo R (2017) MOVPSO: vortex multi-objective particle swarm optimization. Appl Soft Comput 52:1042–1057

    Article  Google Scholar 

  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 5:441–448

    Article  Google Scholar 

  85. http://images.ee.umist.ac.uk/danny/database.html

  86. Hasan MK, Pal CJ (2011) Improving alignment of faces for recognition. In: 2011 IEEE international symposium on Robotic and Sensors Environments (ROSE), pp 249–254

    Google Scholar 

  87. Hasan MK, Pal C (2014) Experiments on visual information extraction with the faces of wikipedia. In: AAAI 2014

    Google Scholar 

  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 33:1978–1990

    Article  Google Scholar 

  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 391–398

    Google Scholar 

  90. Bainbridge WA, Isola P, Oliva A (2013) The intrinsic memorability of face photographs. J Exp Psychol Gen 142:1323–1334

    Article  Google Scholar 

  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. 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 1–5

    Google Scholar 

  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 343–347

    Google Scholar 

  94. Vieira T, Bottino A, Laurentini A, De Simone M (2014) Detecting siblings in image pairs. Vis Comput 30:1333–1345

    Article  Google Scholar 

  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 937–945

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, ITER, Siksha O Anusandhan, Bhubaneswar, Odisha, 751030, India

    Manoj Kumar Naik & Aneesh Wunnava

Authors
  1. Manoj Kumar Naik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aneesh Wunnava
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aneesh Wunnava .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India

    Mihir Narayan Mohanty

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Naik, M.K., Wunnava, A. (2020). Classical 2D Face Recognition: A Survey on Methods, Face Databases, and Performance Evaluation. In: Mohanty, M., Das, S. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 109. Springer, Singapore. https://doi.org/10.1007/978-981-15-2774-6_45

Download citation

Publish with us

Policies and ethics

Search

Navigation