Advertisement

Age invariant face recognition: a survey on facial aging databases, techniques and effect of aging

  • Manisha M. Sawant
  • Kishor M. Bhurchandi
Article

Abstract

Age invariant face recognition (AIFR) is highly required in many applications like law enforcement, national databases and security. Recognizing faces across aging is difficult even for humans; hence, it presents a unique challenge for computer vision systems. Face recognition under various intra-person variations such as expression, pose and occlusion has been an intensively researched field. However, age invariant face recognition still faces many challenges due to age related biological transformations in presence of the other appearance variations. In this paper, we present a comprehensive review of literature on cross age face recognition. Starting with the biological effects of aging, this paper presents a survey of techniques, effects of aging on performance analysis and facial aging databases. The published AIFR techniques are reviewed and categorized into generative, discriminative and deep learning methods on the basis of face representation and learning techniques. Analysis of the effect of aging on the performance of age-invariant face recognition system is an important dimension. Hence, such analysis is reviewed and summarized. In addition, important facial aging databases are briefly described in terms of the number of subjects and images per subject along with their age ranges. We finally present discussions on the findings, conclusions and future directions for new researchers.

Keywords

Age invariant face recognition Effects of aging Facial aging Generative method Discriminative method Deep learning 

References

  1. Abate AF, Nappi M, Riccio D, Sabatino G (2007) 2D and 3D face recognition: a survey. Pattern Recognit Lett 28:1885–1906CrossRefGoogle Scholar
  2. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28:2037–2041CrossRefGoogle Scholar
  3. Albert AM, Ricanek K, Patterson E (2007) A review of the literature on the aging adult skull and face: implications for forensic science research and applications. Forensic Sci Int 172:1–9CrossRefGoogle Scholar
  4. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720CrossRefGoogle Scholar
  5. Best-Rowden L, Jain AK (2015) A longitudinal study of automatic face recognition. In: 2015 International conference on biometrics (ICB). IEEE, pp 214–221Google Scholar
  6. Best-Rowden L, Jain AK (2018) Longitudinal study of automatic face recognition. IEEE Trans Pattern Anal Mach Intell 40(1):148–162CrossRefGoogle Scholar
  7. Bianco S (2017) Large age-gap face verification by feature injection in deep networks. Pattern Recogn Lett 90:36–42CrossRefGoogle Scholar
  8. Biswas S, Aggarwal G, Ramanathan N, Chellappa R (2008) A non-generative approach for face recognition across aging. In: 2nd IEEE international conference on biometrics: theory, applications and systems, 2008 (BTAS 2008). IEEE, pp 1–6Google Scholar
  9. Blanz V, Vetter T (1999) A morphable model for the synthesis of 3D faces. In: Proceedings of the 26th annual conference on computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co, pp 187–194Google Scholar
  10. Chai X, Shan S, Chen X, Gao W (2007) Locally linear regression for pose-invariant face recognition. IEEE Trans Image Process 16:1716–1725MathSciNetCrossRefGoogle Scholar
  11. Chen D, Cao X, Wen F, Sun J (2013) Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3025–3032Google Scholar
  12. Chen B, Chen C, Hsu W (2014) Cross-Age Celebrity Dataset (CACD)Google Scholar
  13. Chen B-C, Chen C-S, Hsu WH (2014) Cross-age reference coding for age-invariant face recognition and retrieval. In: European conference on computer vision. Springer, Berlin, pp 768–783Google Scholar
  14. Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61:38–59CrossRefGoogle Scholar
  15. Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 23:681–685CrossRefGoogle Scholar
  16. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05). IEEE, pp 886–893Google Scholar
  17. Ding C, Tao D (2016) A comprehensive survey on pose-invariant face recognition. ACM Trans Intell Syst Technol 7:37CrossRefGoogle Scholar
  18. Ebner NC, Riediger M, Lindenberger U (2010) FACES—a database of facial expressions in young, middle-aged, and older women and men: development and validation. Behav Res Methods 42:351–362CrossRefGoogle Scholar
  19. Eidinger E, Enbar R, Hassner T (2014) Age and gender estimation of unfiltered faces. IEEE Trans Inf Forensics Secur 9:2170–2179CrossRefGoogle Scholar
  20. FaceVACS (2010) Software Developer Kit, Cognitec Systems GmbH. https://www.cognitec-systems.de
  21. Farage M, Miller K, Elsner P, Maibach H (2008) Intrinsic and extrinsic factors in skin ageing: a review. Int J Cosmet Sci 30:87–95CrossRefGoogle Scholar
  22. Farkas L (1994) Anthropometry of the head and face. Raven Press, New York, p XIXGoogle Scholar
  23. Farkas LG, Munro IR (1987) Anthropometric facial proportions in medicine. Charles C. Thomas, Springfield, ILGoogle Scholar
  24. Feng S, Lang C, Feng J, Wang T, Luo J (2017) Human facial age estimation by cost-sensitive label ranking and trace norm regularization. IEEE Trans Multimed 19:136–148CrossRefGoogle Scholar
  25. FGNET FG-NET Aging Database (2010). http://www.fgnet.rsunit.com
  26. French S (1985) An introduction to latent variable models. Monographs on statistics and applied probability. J Oper Res Soc 36(5):453Google Scholar
  27. Fu Y, Guo G, Huang TS (2010) Age synthesis and estimation via faces: a survey. IEEE Trans Pattern Anal Mach Intell 32:1955–1976CrossRefGoogle Scholar
  28. Gallagher AC, Chen T (2009) Understanding images of groups of people. In: IEEE conference on computer vision and pattern recognition, 2009 (CVPR 2009). IEEE, pp 256–263Google Scholar
  29. Geng X, Zhou Z-H, Zhang Y, Li G, Dai H (2006) Learning from facial aging patterns for automatic age estimation. In: Proceedings of the 14th ACM international conference on Multimedia. ACM, pp 307–316Google Scholar
  30. Geng X, Zhou Z-H, Smith-Miles K (2007) Automatic age estimation based on facial aging patterns. IEEE Trans Pattern Anal Mach Intell 29:2234–2240CrossRefGoogle Scholar
  31. Geng X, Fu Y, Smith-Miles K (2010) Automatic facial age estimation. In: 11th Pacific rim international conference on artificial intelligence, pp 1–130Google Scholar
  32. Geng X, Yin C, Zhou Z-H (2013) Facial age estimation by learning from label distributions. IEEE Trans Pattern Anal Mach Intell 35:2401–2412CrossRefGoogle Scholar
  33. Gong D, Li Z, Lin D, Liu J, Tang X (2013) Hidden factor analysis for age invariant face recognition. In: Proceedings of the IEEE international conference on computer vision, pp 2872–2879Google Scholar
  34. Gong D, Li Z, Tao D, Liu J, Li X (2015) A maximum entropy feature descriptor for age invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5289–5297Google Scholar
  35. Guo G, Wang X (2012) A study on human age estimation under facial expression changes. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2547–2553Google Scholar
  36. He X, Niyogi P (2003) Locality preserving projections. In: Advances in neutral information processing systems 16 NIPS2003Google Scholar
  37. Huang FJ, Zhou Z, Zhang H-J, Chen T (2000) Pose invariant face recognition. In: Proceedings of the fourth IEEE international conference on automatic face and gesture recognition, 2000. IEEE, pp 245–250Google Scholar
  38. Huang GB, Mattar M, Berg T, Learned-Miller E (2008) Labeled faces in the wild: a database forstudying face recognition in unconstrained environments. In: Workshop on faces in ‘Real-Life’ images: detection, alignment, and recognitionGoogle Scholar
  39. Iqbal MTB, Chae O (2018) Mining wrinkle-patterns with local edge-prototypic pattern (LEPP) descriptor for the recognition of human age-groupsGoogle Scholar
  40. Iqbal MTB, Ryu B, Song G, Chae O (2016) Positional Ternary Pattern (PTP): an edge based image descriptor for human age recognition. In: 2016 IEEE international conference on consumer electronics (ICCE). IEEE, pp 289–292Google Scholar
  41. Iqbal MTB, Shoyaib M, Ryu B, Abdullah-Al-Wadud M, Chae O (2017) Directional age-primitive pattern (DAPP) for human age group recognition and age estimation. IEEE Trans Inf Forensics Secur 12:2505–2517CrossRefGoogle Scholar
  42. Jain A, Bolle R, Pankanti S (2006) Biometrics: personal identification in networked society, vol 479. Springer, BerlinGoogle Scholar
  43. Juefei-Xu F, Luu K, Savvides M, Bui TD, Suen CY (2011) Investigating age invariant face recognition based on periocular biometrics. In: 2011 International joint conference on biometrics (IJCB). IEEE, pp 1–7Google Scholar
  44. Klare B, Jain AK (2011) Face recognition across time lapse: on learning feature subspaces. In: 2011 International joint conference on biometrics (IJCB). IEEE, pp 1–8Google Scholar
  45. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105Google Scholar
  46. Lanitis A (2009a) Facial biometric templates and aging: problems and challenges for artificial intelligence. In: AIAI workshops, pp 142–149Google Scholar
  47. Lanitis A (2009b) A survey of the effects of aging on biometric identity verification. Int J Biometr 2:34–52CrossRefGoogle Scholar
  48. Lanitis A, Taylor CJ, Cootes TF (2002) Toward automatic simulation of aging effects on face images. IEEE Trans Pattern Anal Mach Intell 24:442–455CrossRefGoogle Scholar
  49. Lanitis A, Draganova C, Christodoulou C (2004) Comparing different classifiers for automatic age estimation. IEEE Trans Syst Man Cybern B Cybern 34:621–628CrossRefGoogle Scholar
  50. Li Z, Park U, Jain AK (2011) A discriminative model for age invariant face recognition. IEEE Trans Inf Forensics Secur 6:1028–1037CrossRefGoogle Scholar
  51. Li Y, Wang G, Lin L, Chang HA (2015) Deep joint learning approach for age invariant face verification. In: CCF Chinese conference on computer vision. Springer, Berlin, pp 296–305Google Scholar
  52. Li Z, Gong D, Li X, Tao D (2016) Aging face recognition: a hierarchical learning model based on local patterns selection. IEEE Trans Image Process 25:2146–2154MathSciNetCrossRefGoogle Scholar
  53. Li H, Zou H, Hu H (2017) Modified hidden factor analysis for cross-age face recognition. IEEE Signal Process Lett 24:465–469CrossRefGoogle Scholar
  54. Liang Y, Wang X, Zhang L, Wang Z (2014) A hierarchical framework for facialage estimation. Math Probl Eng 2014:242846Google Scholar
  55. Ling H, Soatto S, Ramanathan N, Jacobs DW (2007) A study of face recognition as people age. In: 2007 IEEE 11th international conference on computer vision. IEEE, pp 1–8Google Scholar
  56. Ling H, Soatto S, Ramanathan N, Jacobs DW (2010) Face verification across age progression using discriminative methods. IEEE Trans Inf Forensics Secur 5:82–91CrossRefGoogle Scholar
  57. Lou Z, Alnajar F, Alvarez JM, Hu N, Gevers T (2018) Expression-invariant age estimation using structured learning. IEEE Trans Pattern Anal Mach Intell 40:365–375CrossRefGoogle Scholar
  58. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110CrossRefGoogle Scholar
  59. Mahalingam G, Kambhamettu C (2010) Age invariant face recognition using graph matching. In: 2010 Fourth IEEE international conference on biometrics: theory applications and systems (BTAS). IEEE, pp 1–7Google Scholar
  60. Mark LS, Todd JT, Shaw RE (1981) Perception of growth: a geometric analysis of how different styles of change are distinguished. J Exp Psychol Hum Percept Perform 7:855CrossRefGoogle Scholar
  61. Moon TK (1996) The expectation-maximization algorithm. IEEE Signal Process Mag 13:47–60CrossRefGoogle Scholar
  62. Muller SD, Marchetto J, Airaghi S, Kournoutsakos P (2002) Optimization based on bacterial chemotaxis. IEEE Trans Evol Comput 6:16–29CrossRefGoogle Scholar
  63. Nixon N, Galassi P, Art NYMoM (2007) The Brown sisters: thirty-three years. Museum of Modern ArtGoogle Scholar
  64. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–987CrossRefGoogle Scholar
  65. Otto C, Han H, Jain A (2012) How does aging affect facial components? In: European conference on computer vision. Springer, Berlin, pp 189–198CrossRefGoogle Scholar
  66. Ouyang W et al (2015) Deepid-net: deformable deep convolutional neural networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2403–2412Google Scholar
  67. Panis G, Lanitis A (2014) An overview of research activities in facial age estimation using the FG-NET aging database. In: European conference on computer vision. Springer, Berlin, pp 737–750Google Scholar
  68. Park U, Tong Y, Jain AK (2008) Face recognition with temporal invariance: a 3d aging model. In: 8th IEEE international conference on automatic face & gesture recognition, 2008 (FG’08). IEEE, pp 1–7Google Scholar
  69. Park U, Tong Y, Jain AK (2010) Age-invariant face recognition. IEEE Trans Pattern Anal Mach Intell 32:947–954CrossRefGoogle Scholar
  70. Parlewar M, Patil H, Bhurchandi K (2016) A novel quantized gradient direction based face image representation and recognition technique. In: 2016 Twenty second national conference on communication (NCC). IEEE, pp 1–6Google Scholar
  71. Patil H, Kothari A, Bhurchandi K (2015) 3-D face recognition: features, databases, algorithms and challenges. Artif Intell Rev 44:393–441CrossRefGoogle Scholar
  72. Patil HY, Kothari AG, Bhurchandi KM (2016) Expression invariant face recognition using local binary patterns and contourlet transform. Optik-Int J Light Electron Opt 127:2670–2678CrossRefGoogle Scholar
  73. Patterson E, Ricanek K, Albert M, Boone E (2006) Automatic representation of adult aging in facial images. In: Proceedings of the IASTED international conference visualization, imaging, and image processing, pp 171–176Google Scholar
  74. Penev PS, Atick JJ (1996) Local feature analysis: a general statistical theory for object representation. Netw Comput Neural Syst 7:477–500CrossRefGoogle Scholar
  75. Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22:1090–1104CrossRefGoogle Scholar
  76. Ramanathan N, Chellappa R (2006) Modeling age progression in young faces. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06). IEEE, pp 387–394Google Scholar
  77. Ramanathan N, Chellappa R (2008) Modeling shape and textural variations in aging faces. In: 8th IEEE international conference on automatic face & gesture recognition, 2008 (FG’08). IEEE, pp 1–8Google Scholar
  78. Ramanathan N, Chellappa R, Biswas S (2009) Computational methods for modeling facial aging: a survey. J Vis Lang Comput 20:131–144CrossRefGoogle Scholar
  79. Ricanek K, Tesafaye T (2006) Morph: a longitudinal image database of normal adult age-progression. In: 7th International conference on automatic face and gesture recognition (FGR06). IEEE, pp 341–345Google Scholar
  80. Ricanek K, Sethuram A, Patterson EK, Albert AM, Boone EJ (2008) Craniofacial aging Wiley handbook of science and technology for homeland securityGoogle Scholar
  81. Rizvi SA, Phillips PJ, Moon H (1998) A verification protocol and statistical performance analysis for face recognition algorithms. In: Proceedings of the 1998 IEEE computer society conference on computer vision and pattern recognition. IEEE, pp 833–838Google Scholar
  82. Singh M, Nagpal S, Singh R, Vatsa M (2014) On recognizing face images with weight and age variations. IEEE Access 2:822–830CrossRefGoogle Scholar
  83. Sun Y, Chen Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification. In: Advances in neural information processing systems, pp 1988–1996Google Scholar
  84. Sungatullina D, Lu J, Wang G, Moulin P (2013) Multiview discriminative learning for age-invariant face recognition. In: 2013 10th IEEE international conference and workshops on automatic face and gesture recognition (FG). IEEE, pp 1–6Google Scholar
  85. Thompson DW (1917) On growth and form Cambridge, EnglandGoogle Scholar
  86. Turk MA, Pentland AP (1991) Face recognition using eigenfaces. In: Proceedings of the CVPR’91, IEEE computer society conference on computer vision and pattern recognition, 1991. IEEE, pp 586–591Google Scholar
  87. Uludag U, Ross A, Jain A (2004) Biometric template selection and update: a case study in fingerprints. Pattern Recogn 37:1533–1542CrossRefGoogle Scholar
  88. Wang J, Shang Y, Su G, Lin X (2006) Age simulation for face recognition. In: 18th International conference on pattern recognition (ICPR’06). IEEE, pp 913–916Google Scholar
  89. Wen Y, Li Z, Qiao Y (2016) Latent factor guided convolutional neural networks for age-invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4893–4901Google Scholar
  90. Xu C, Liu Q, Ye M (2017) Age invariant face recognition and retrieval by coupled auto-encoder networks. Neurocomputing 222:62–71CrossRefGoogle Scholar
  91. Yadav D, Vatsa M, Singh R, Tistarelli M (2013) Bacteria foraging fusion for face recognition across age progression. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 173–179Google Scholar
  92. Yang J, Zhang D, J-y Yang, Niu B (2007) Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans Pattern Anal Mach Intell 29:650–664CrossRefGoogle Scholar
  93. Yi D, Lei Z, Liao S, Li SZ (2014) Learning face representation from scratch arXiv preprint arXiv:14117923

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringVisvesvaraya National Institute of TechnologyNagpurIndia

Personalised recommendations