Skip to main content
Top
Published in: Artificial Intelligence Review 2/2019

13-10-2018

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

Authors: Manisha M. Sawant, Kishor M. Bhurchandi

Published in: Artificial Intelligence Review | Issue 2/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
go back to reference Abate AF, Nappi M, Riccio D, Sabatino G (2007) 2D and 3D face recognition: a survey. Pattern Recognit Lett 28:1885–1906CrossRef Abate AF, Nappi M, Riccio D, Sabatino G (2007) 2D and 3D face recognition: a survey. Pattern Recognit Lett 28:1885–1906CrossRef
go back to reference Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28:2037–2041CrossRefMATH Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28:2037–2041CrossRefMATH
go back to reference 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 172:1–9CrossRef 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 172:1–9CrossRef
go back to reference Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720CrossRef Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720CrossRef
go back to reference Best-Rowden L, Jain AK (2015) A longitudinal study of automatic face recognition. In: 2015 International conference on biometrics (ICB). IEEE, pp 214–221 Best-Rowden L, Jain AK (2015) A longitudinal study of automatic face recognition. In: 2015 International conference on biometrics (ICB). IEEE, pp 214–221
go back to reference Best-Rowden L, Jain AK (2018) Longitudinal study of automatic face recognition. IEEE Trans Pattern Anal Mach Intell 40(1):148–162CrossRef Best-Rowden L, Jain AK (2018) Longitudinal study of automatic face recognition. IEEE Trans Pattern Anal Mach Intell 40(1):148–162CrossRef
go back to reference Bianco S (2017) Large age-gap face verification by feature injection in deep networks. Pattern Recogn Lett 90:36–42CrossRef Bianco S (2017) Large age-gap face verification by feature injection in deep networks. Pattern Recogn Lett 90:36–42CrossRef
go back to reference 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 1–6 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 1–6
go back to reference 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 187–194 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 187–194
go back to reference Chai X, Shan S, Chen X, Gao W (2007) Locally linear regression for pose-invariant face recognition. IEEE Trans Image Process 16:1716–1725MathSciNetCrossRef Chai X, Shan S, Chen X, Gao W (2007) Locally linear regression for pose-invariant face recognition. IEEE Trans Image Process 16:1716–1725MathSciNetCrossRef
go back to reference 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 3025–3032 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 3025–3032
go back to reference Chen B, Chen C, Hsu W (2014) Cross-Age Celebrity Dataset (CACD) Chen B, Chen C, Hsu W (2014) Cross-Age Celebrity Dataset (CACD)
go back to reference 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 768–783 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 768–783
go back to reference Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61:38–59CrossRef Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61:38–59CrossRef
go back to reference Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 23:681–685CrossRef Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 23:681–685CrossRef
go back to reference 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 886–893 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 886–893
go back to reference Ding C, Tao D (2016) A comprehensive survey on pose-invariant face recognition. ACM Trans Intell Syst Technol 7:37CrossRef Ding C, Tao D (2016) A comprehensive survey on pose-invariant face recognition. ACM Trans Intell Syst Technol 7:37CrossRef
go back to reference 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 42:351–362CrossRef 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 42:351–362CrossRef
go back to reference Eidinger E, Enbar R, Hassner T (2014) Age and gender estimation of unfiltered faces. IEEE Trans Inf Forensics Secur 9:2170–2179CrossRef Eidinger E, Enbar R, Hassner T (2014) Age and gender estimation of unfiltered faces. IEEE Trans Inf Forensics Secur 9:2170–2179CrossRef
go back to reference Farage M, Miller K, Elsner P, Maibach H (2008) Intrinsic and extrinsic factors in skin ageing: a review. Int J Cosmet Sci 30:87–95CrossRef Farage M, Miller K, Elsner P, Maibach H (2008) Intrinsic and extrinsic factors in skin ageing: a review. Int J Cosmet Sci 30:87–95CrossRef
go back to reference Farkas L (1994) Anthropometry of the head and face. Raven Press, New York, p XIX Farkas L (1994) Anthropometry of the head and face. Raven Press, New York, p XIX
go back to reference Farkas LG, Munro IR (1987) Anthropometric facial proportions in medicine. Charles C. Thomas, Springfield, IL Farkas LG, Munro IR (1987) Anthropometric facial proportions in medicine. Charles C. Thomas, Springfield, IL
go back to reference 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 19:136–148CrossRef 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 19:136–148CrossRef
go back to reference French S (1985) An introduction to latent variable models. Monographs on statistics and applied probability. J Oper Res Soc 36(5):453 French S (1985) An introduction to latent variable models. Monographs on statistics and applied probability. J Oper Res Soc 36(5):453
go back to reference Fu Y, Guo G, Huang TS (2010) Age synthesis and estimation via faces: a survey. IEEE Trans Pattern Anal Mach Intell 32:1955–1976CrossRef Fu Y, Guo G, Huang TS (2010) Age synthesis and estimation via faces: a survey. IEEE Trans Pattern Anal Mach Intell 32:1955–1976CrossRef
go back to reference 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 256–263 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 256–263
go back to reference 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 307–316 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 307–316
go back to reference Geng X, Zhou Z-H, Smith-Miles K (2007) Automatic age estimation based on facial aging patterns. IEEE Trans Pattern Anal Mach Intell 29:2234–2240CrossRef Geng X, Zhou Z-H, Smith-Miles K (2007) Automatic age estimation based on facial aging patterns. IEEE Trans Pattern Anal Mach Intell 29:2234–2240CrossRef
go back to reference Geng X, Fu Y, Smith-Miles K (2010) Automatic facial age estimation. In: 11th Pacific rim international conference on artificial intelligence, pp 1–130 Geng X, Fu Y, Smith-Miles K (2010) Automatic facial age estimation. In: 11th Pacific rim international conference on artificial intelligence, pp 1–130
go back to reference Geng X, Yin C, Zhou Z-H (2013) Facial age estimation by learning from label distributions. IEEE Trans Pattern Anal Mach Intell 35:2401–2412CrossRef Geng X, Yin C, Zhou Z-H (2013) Facial age estimation by learning from label distributions. IEEE Trans Pattern Anal Mach Intell 35:2401–2412CrossRef
go back to reference 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 2872–2879 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 2872–2879
go back to reference 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 5289–5297 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 5289–5297
go back to reference 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 2547–2553 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 2547–2553
go back to reference He X, Niyogi P (2003) Locality preserving projections. In: Advances in neutral information processing systems 16 NIPS2003 He X, Niyogi P (2003) Locality preserving projections. In: Advances in neutral information processing systems 16 NIPS2003
go back to reference 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 245–250 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 245–250
go back to reference 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 recognition 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 recognition
go back to reference Iqbal MTB, Chae O (2018) Mining wrinkle-patterns with local edge-prototypic pattern (LEPP) descriptor for the recognition of human age-groups Iqbal MTB, Chae O (2018) Mining wrinkle-patterns with local edge-prototypic pattern (LEPP) descriptor for the recognition of human age-groups
go back to reference 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 289–292 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 289–292
go back to reference 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 12:2505–2517CrossRef 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 12:2505–2517CrossRef
go back to reference Jain A, Bolle R, Pankanti S (2006) Biometrics: personal identification in networked society, vol 479. Springer, Berlin Jain A, Bolle R, Pankanti S (2006) Biometrics: personal identification in networked society, vol 479. Springer, Berlin
go back to reference 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 1–7 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 1–7
go back to reference Klare B, Jain AK (2011) Face recognition across time lapse: on learning feature subspaces. In: 2011 International joint conference on biometrics (IJCB). IEEE, pp 1–8 Klare B, Jain AK (2011) Face recognition across time lapse: on learning feature subspaces. In: 2011 International joint conference on biometrics (IJCB). IEEE, pp 1–8
go back to reference Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105 Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
go back to reference Lanitis A (2009a) Facial biometric templates and aging: problems and challenges for artificial intelligence. In: AIAI workshops, pp 142–149 Lanitis A (2009a) Facial biometric templates and aging: problems and challenges for artificial intelligence. In: AIAI workshops, pp 142–149
go back to reference Lanitis A (2009b) A survey of the effects of aging on biometric identity verification. Int J Biometr 2:34–52CrossRef Lanitis A (2009b) A survey of the effects of aging on biometric identity verification. Int J Biometr 2:34–52CrossRef
go back to reference Lanitis A, Taylor CJ, Cootes TF (2002) Toward automatic simulation of aging effects on face images. IEEE Trans Pattern Anal Mach Intell 24:442–455CrossRef Lanitis A, Taylor CJ, Cootes TF (2002) Toward automatic simulation of aging effects on face images. IEEE Trans Pattern Anal Mach Intell 24:442–455CrossRef
go back to reference Lanitis A, Draganova C, Christodoulou C (2004) Comparing different classifiers for automatic age estimation. IEEE Trans Syst Man Cybern B Cybern 34:621–628CrossRef Lanitis A, Draganova C, Christodoulou C (2004) Comparing different classifiers for automatic age estimation. IEEE Trans Syst Man Cybern B Cybern 34:621–628CrossRef
go back to reference Li Z, Park U, Jain AK (2011) A discriminative model for age invariant face recognition. IEEE Trans Inf Forensics Secur 6:1028–1037CrossRef Li Z, Park U, Jain AK (2011) A discriminative model for age invariant face recognition. IEEE Trans Inf Forensics Secur 6:1028–1037CrossRef
go back to reference 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 296–305 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 296–305
go back to reference 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 25:2146–2154MathSciNetCrossRefMATH 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 25:2146–2154MathSciNetCrossRefMATH
go back to reference Li H, Zou H, Hu H (2017) Modified hidden factor analysis for cross-age face recognition. IEEE Signal Process Lett 24:465–469CrossRef Li H, Zou H, Hu H (2017) Modified hidden factor analysis for cross-age face recognition. IEEE Signal Process Lett 24:465–469CrossRef
go back to reference Liang Y, Wang X, Zhang L, Wang Z (2014) A hierarchical framework for facialage estimation. Math Probl Eng 2014:242846 Liang Y, Wang X, Zhang L, Wang Z (2014) A hierarchical framework for facialage estimation. Math Probl Eng 2014:242846
go back to reference 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 1–8 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 1–8
go back to reference Ling H, Soatto S, Ramanathan N, Jacobs DW (2010) Face verification across age progression using discriminative methods. IEEE Trans Inf Forensics Secur 5:82–91CrossRef Ling H, Soatto S, Ramanathan N, Jacobs DW (2010) Face verification across age progression using discriminative methods. IEEE Trans Inf Forensics Secur 5:82–91CrossRef
go back to reference Lou Z, Alnajar F, Alvarez JM, Hu N, Gevers T (2018) Expression-invariant age estimation using structured learning. IEEE Trans Pattern Anal Mach Intell 40:365–375CrossRef Lou Z, Alnajar F, Alvarez JM, Hu N, Gevers T (2018) Expression-invariant age estimation using structured learning. IEEE Trans Pattern Anal Mach Intell 40:365–375CrossRef
go back to reference Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110CrossRef Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110CrossRef
go back to reference 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 1–7 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 1–7
go back to reference 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 7:855CrossRef 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 7:855CrossRef
go back to reference Moon TK (1996) The expectation-maximization algorithm. IEEE Signal Process Mag 13:47–60CrossRef Moon TK (1996) The expectation-maximization algorithm. IEEE Signal Process Mag 13:47–60CrossRef
go back to reference Muller SD, Marchetto J, Airaghi S, Kournoutsakos P (2002) Optimization based on bacterial chemotaxis. IEEE Trans Evol Comput 6:16–29CrossRef Muller SD, Marchetto J, Airaghi S, Kournoutsakos P (2002) Optimization based on bacterial chemotaxis. IEEE Trans Evol Comput 6:16–29CrossRef
go back to reference Nixon N, Galassi P, Art NYMoM (2007) The Brown sisters: thirty-three years. Museum of Modern Art Nixon N, Galassi P, Art NYMoM (2007) The Brown sisters: thirty-three years. Museum of Modern Art
go back to reference 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 24:971–987CrossRefMATH 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 24:971–987CrossRefMATH
go back to reference Otto C, Han H, Jain A (2012) How does aging affect facial components? In: European conference on computer vision. Springer, Berlin, pp 189–198 Otto C, Han H, Jain A (2012) How does aging affect facial components? In: European conference on computer vision. Springer, Berlin, pp 189–198
go back to reference 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 2403–2412 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 2403–2412
go back to reference 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 737–750 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 737–750
go back to reference 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 1–7 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 1–7
go back to reference Park U, Tong Y, Jain AK (2010) Age-invariant face recognition. IEEE Trans Pattern Anal Mach Intell 32:947–954CrossRef Park U, Tong Y, Jain AK (2010) Age-invariant face recognition. IEEE Trans Pattern Anal Mach Intell 32:947–954CrossRef
go back to reference 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 1–6 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 1–6
go back to reference Patil H, Kothari A, Bhurchandi K (2015) 3-D face recognition: features, databases, algorithms and challenges. Artif Intell Rev 44:393–441CrossRef Patil H, Kothari A, Bhurchandi K (2015) 3-D face recognition: features, databases, algorithms and challenges. Artif Intell Rev 44:393–441CrossRef
go back to reference Patil HY, Kothari AG, Bhurchandi KM (2016) Expression invariant face recognition using local binary patterns and contourlet transform. Optik-Int J Light Electron Opt 127:2670–2678CrossRef Patil HY, Kothari AG, Bhurchandi KM (2016) Expression invariant face recognition using local binary patterns and contourlet transform. Optik-Int J Light Electron Opt 127:2670–2678CrossRef
go back to reference 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 171–176 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 171–176
go back to reference Penev PS, Atick JJ (1996) Local feature analysis: a general statistical theory for object representation. Netw Comput Neural Syst 7:477–500CrossRefMATH Penev PS, Atick JJ (1996) Local feature analysis: a general statistical theory for object representation. Netw Comput Neural Syst 7:477–500CrossRefMATH
go back to reference Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22:1090–1104CrossRef Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22:1090–1104CrossRef
go back to reference 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 387–394 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 387–394
go back to reference 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 1–8 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 1–8
go back to reference Ramanathan N, Chellappa R, Biswas S (2009) Computational methods for modeling facial aging: a survey. J Vis Lang Comput 20:131–144CrossRef Ramanathan N, Chellappa R, Biswas S (2009) Computational methods for modeling facial aging: a survey. J Vis Lang Comput 20:131–144CrossRef
go back to reference 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 341–345 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 341–345
go back to reference Ricanek K, Sethuram A, Patterson EK, Albert AM, Boone EJ (2008) Craniofacial aging Wiley handbook of science and technology for homeland security Ricanek K, Sethuram A, Patterson EK, Albert AM, Boone EJ (2008) Craniofacial aging Wiley handbook of science and technology for homeland security
go back to reference 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 833–838 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 833–838
go back to reference Singh M, Nagpal S, Singh R, Vatsa M (2014) On recognizing face images with weight and age variations. IEEE Access 2:822–830CrossRef Singh M, Nagpal S, Singh R, Vatsa M (2014) On recognizing face images with weight and age variations. IEEE Access 2:822–830CrossRef
go back to reference 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 1988–1996 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 1988–1996
go back to reference 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 1–6 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 1–6
go back to reference Thompson DW (1917) On growth and form Cambridge, England Thompson DW (1917) On growth and form Cambridge, England
go back to reference 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 586–591 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 586–591
go back to reference Uludag U, Ross A, Jain A (2004) Biometric template selection and update: a case study in fingerprints. Pattern Recogn 37:1533–1542CrossRefMATH Uludag U, Ross A, Jain A (2004) Biometric template selection and update: a case study in fingerprints. Pattern Recogn 37:1533–1542CrossRefMATH
go back to reference 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 913–916 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 913–916
go back to reference 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 4893–4901 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 4893–4901
go back to reference Xu C, Liu Q, Ye M (2017) Age invariant face recognition and retrieval by coupled auto-encoder networks. Neurocomputing 222:62–71CrossRef Xu C, Liu Q, Ye M (2017) Age invariant face recognition and retrieval by coupled auto-encoder networks. Neurocomputing 222:62–71CrossRef
go back to reference 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 173–179 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 173–179
go back to reference 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 29:650–664CrossRef 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 29:650–664CrossRef
Metadata
Title
Age invariant face recognition: a survey on facial aging databases, techniques and effect of aging
Authors
Manisha M. Sawant
Kishor M. Bhurchandi
Publication date
13-10-2018
Publisher
Springer Netherlands
Published in
Artificial Intelligence Review / Issue 2/2019
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-018-9661-z

Other articles of this Issue 2/2019

Artificial Intelligence Review 2/2019 Go to the issue

Premium Partner