Abstract
Soft biometrics has emerged out to be a new area of interest for the researchers due to its growing real-world applications. It includes the estimation of demographic traits like age, gender, scars, ethnicity. Moreover, researchers are trying to develop models which can accurately estimate the age or the age group of a person using different biometric traits. Presently, neural networks proves out to give the best classification results for age estimation using human faces. Hence, in this paper, we have surveyed and compared all the neural network models developed and implemented for facial age estimation from 2010 to 2019. We have precisely compared all twenty-three different research works done so far to estimate age from human faces using neural networks. Most of the works are based on convolutional neural networks and a few are based on feed forward back propagation and autoencoders. Important details, issues and results of each work are thoroughly discussed for better knowledge of interested researchers. This paper also includes details on other classification techniques for facial age estimation to give an overall idea of all additional techniques adopted by the scientists till date. Details like neural network model names, datasets used, main contributions, evaluation metrics and results are adopted for a tabular and easy to understand comparison study. Finally, the paper concludes by mentioning the other relevant future research tasks that can be done in this challenging area of research.
Similar content being viewed by others
References
Akinyemi JD, Onifade OFW (2016) An ethnic-specific age group ranking approach to facial age estimation using raw pixel features. In: Proceedings of IEEE symposium on technologies for homeland security, Waltham, 2016, pp 1–6
Bastanfard A, Abbasian Nik M, Dehshibi M.M. (2007) Iranian face database with age, pose and expression. In: Proceedings of IEEE international conference on machine vision, pp 50–55
Bay H, Tuytelaars T, Gool LV (2006) SURF: speeded up robust features. Comput Vis ECCV 3951(1):404–417
Beymer D, Poggio T (1996) Image representations for visual learning. Science 272(5270):1905–1909
Cai D, He X, Han J, Zhang HJ (2006) Orthogonal Laplacian faces for face recognition. Trans Image Process 15(11):3608–3614
Cai D, He X, Zhou K, Han J, Bao H (2007) Locality sensitive discriminant analysis. In: International joint conference on artificial intelligence, pp 708–713
Chen J-C, Kumar A, Ranjan R, Patel VM, Alavi A, Chellappa R (2016) A cascaded convolutional neural network for age estimation of unconstrained faces. In: Proceedings of IEEE conference on biometrics, theory, applications and systems. https://doi.org/10.1109/btas.2016.7791154
Chen S, Zhang C, Dong M, Lee J, Rao M (2017) Using ranking-CNN for age estimation. In: IEEE conference on computer vision and pattern recognition (CVPR), https://doi.org/10.1109/cvpr.2017.86
Choi SE, Lee YJ, Lee SJ, Park KR, Kim J (2011) Age estimation using hierarchical classifier based on global and local features. Pattern Recognit 44(6):1262–1281
Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models—their training and application. Comput Vis Image Underst 61(1):38–59
Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 23(6):681–685
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, pp 886–893
Das A, Pal U, Blumenstein M, Ballester MF (2013) Sclera recognition - a survey. In: IAPR Asian conference on pattern recognition (ACPR), pp 917–921
Dehshibi MM, Bastanfard A (2010) A new algorithm for age recognition from facial images. Signal Process 90(8):2431–2444
Dong Y, Liu Y, Lian S (2015) Automatic age estimation based on deep learning algorithm. Neurocomputing 187:4–10
Duan M, Li K, Yang C, Li K (2018) A hybrid deep learning CNN–ELM for age and gender classification. Neurocomputing 275:448–461
Escalera S, Fabian J, Pardo P, Baro X, Gonzalez J, Escalante HJ, Misevic D, Steiner U, Guyon I (2015) Chalearn looking at people 2015: apparent age and cultural event recognition datasets and results. In: The IEEE international conference on computer vision (ICCV) workshops, pp 243–251
Escalera S, Torres M, Martnez B, Bar X, Escalante HJ, Guyon I, Tzimiropoulos G, Corneanu C, Oliu M, Bagheri MA, Valstar M (2016) Chalearn looking at people and faces of the world: face analysis workshop and challenge 2016. In: IEEE conference on computer vision and pattern recognition workshops, pp 706–713
Farkas LG (1994) Anthropometry of the head and face. Raven Press. https://doi.org/10.1016/0278-2391(95)90208-2
Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129
Fisher RA (1938) The statistical utilization of multiple measurements. Ann Eugen 8:376–386
Fu Y, Xu Y, Huang TS (2007) Estimating human ages by manifold analysis of face pictures and regression on aging features. In: Proceedings of IEEE conference multimedia and expo, pp 1383–1386
Gabor D (1946) Theory of communication. J Inst Electr Electron Eng 93:429–457
Gallagher AC, Chen T (2009) Understanding images of groups of people. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 256–263
Gao F, Ai H (2009) Face age classification on consumer images with Gabor feature and fuzzy LDA method. In: Proceedings of 3rd international conference on advances in biometrics, lecture notes in computer science, Springer, Alghero, pp 132–141
Geng X (2016) Label distribution learning. IEEE Trans Knowl Data Eng 28(7):1734–1748
Geng X, Ji R (2013) Label distribution learning. In: IEEE conference on data mining workshops, pp 377–383
Geng X, Zhou Z, SmithMiles K (2007) Automatic age estimation based on facial aging patterns. IEEE Trans Pattern Anal Mach Intell (TPAMI) 29(12):2234–2240
Gunay A, Nabiyev VV (2008) Automatic age classification with LBP. In: Proceedings of 23rd international symposium of computer and information sciences, IEEE, Istanbul, 2008, pp 1–4
Gunay A, Nabiyev VV (2015) Facial age estimation based on decision level fusion of AMM, LBP and Gabor features. Int J Adv Comput Sci Appl 6(8):19–26
Guo G, Mu G (2011) Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression. In: IEEE conference on computer vision and pattern recognition (CVPR) pp 657–664
Guo G, Mu G (2013) Joint estimation of age, gender and ethnicity: CCA vs PLS. In: Proceedings of IEEE conference on face and gesture recognition (Shanghai, 2013), pp 1–6
Guo G, Fu Y, Dyer C, Huang T (2008a) Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Trans Image Process 17(7):1178–1188
Guo G, Fu Y, Huang TS, Dyer C (2008b) A probabilistic fusion approach to human age prediction. In: Proceedings of IEEE in conference on computer vision and pattern recognition-semantic learning and applications multimedia workshop, pp 1–6
Guo G, Mu G, Fu Y, Huang TS (2009) Human age estimation using bio inspired features. In: Proceedings of IEEE conference on computer vision and pattern recognition. (IEEE, Miami), pp 112–119
Guo G, Fu Y, Huang TS, Dyer C (2018) Locally adjusted robust regression for human age estimation. In: Proceedings of IEEE workshop on applications of computer vision, pp 19–21. https://doi.org/10.1109/wacv.2008.4544009
Han H, Charles O, Liu X, Jain AK (2015) Demographic estimation from face images: human vs machine performance. IEEE Trans Pattern Anal Mach Intell 37(1):1148–1161
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), pp. 770–778
Horng WB, Lee CP, Chen CW (2001) Classification of age groups based on facial features. Tamkang J Sci Eng 4(3):183–191
Hu Z, Wen Y, Wang J, Wang M, Hong R, Yan S (2016) Facial age estimation with age difference. IEEE Trans Image Process 26(7):3087–3097
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501
Huang GB, Ramesh M, Berg T, Miller EL (2007) Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report. University of Massachusetts, Amherst, pp 07–14
Huerta I, Fernandez C, Segura C, Hernando J, Prati A (2015) A deep analysis on age estimation. Pattern Recognit Lett 68(2):239–249
Huo Z, Yang X, Xing C, Zhou Y, Hou P, Lv J, Geng X (2016) Deep age distribution learning for apparent age estimation. In: IEEE conference on computer vision and pattern recognition workshops, pp 722–729
Kang JS, Kim CS, Lee YW, Cho SW, Park KR (2018) Age estimation robust to optical and motion blurring by deep residual CNN. Symmetry 10(4):108. https://doi.org/10.3390/sym10040108
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: NIPS’12 proceedings of the 25th international conference on neural information processing systems, pp 1097–1105
Lanitis A, Draganova C, Christodoulou C (2004) Comparing different classifiers for automatic age estimation. IEEE Trans Man Syst Cybernet 34(1):621–628
Laskar BZ, Ashutosh Majumder S (2015) Artificial neural networks and gene expression programing based age estimation using facial features. J King Saud Univ Comput Inf Sci 27(4):458–467
Levi G, Hassner T (2015) Age and gender classification using convolutional neural networks. In: IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 34–42
Li K, Xing J, Hu W, Maybank SJ (2017) D2C: deep cumulatively and comparatively learning for human age estimation. Pattern Recognit 66:1–460
Liu K-H, Yan S, Kuo JC-C (2014) Age group classification via structured fusion of uncertainty-driven shape features and selected surface features. In: Proceeding of IEEE winter conference on applications of computer vision (WACV), pp 445–452
Liu X, Li S, Kan M, Zhang J, Wu S, Liu W, Han H, Shan S, Chen X (2015) AgeNet: deeply learned regressor and classifier for robust apparent age estimation. In: IEEE international conference on computer vision workshop, pp 258–266
Lu J, Tan Y (2013) Ordinary preserving manifold analysis for human age and head pose estimation. IEEE Trans Hum Mach Syst 43(2):249–258
Malli RC, Aygun M, Ekenel HK (2016) Apparent age estimation using ensemble of deep learning models. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 714–721
Nguyen DT, Cho SR, Park KR (2015) Age estimation-based soft biometrics considering optical blurring based on symmetrical sub-blocks for MLBP. Symmetry 7(4):1882–1913
Niu Z, Zhou M, Wang L, Gao X, Hua G (2016) Ordinal regression with multiple output CNN for age estimation. In: IEEE conference on computer vision and pattern recognition, pp 4920–4928
Ojala T, Pietikäinen M, Mäenpää (2001) A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification. In: Proceedings of 2nd ICAPR, Springer, Rio de Janeiro, pp. 397–406
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Onifade OFW, Akinyemi DJ (2015) A groupwise age ranking framework for human age estimation. Int J Image Gr Signal Process 5:1–12
Panis G, Lanitis A, Tsapatsoulis N, Cootes TF (2016) Overview of research on facial ageing using the FG-NET ageing database. IET Biom 5(2):37–46
Philips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The Feret evaluation methodology for face recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10):1090–1104
Punyani P, Gupta R, Kumar A (2018) Human age-estimation system based on double-level feature fusion of face and gait images. Int J Image Data Fusion Taylor and Francis 9(3):222–236
Zakariya Q, Mallouh AA, Barkana BD (2017) Age and gender classification from speech and face images by jointly fine-tuned deep neural networks. Expert Syst Appl 85(C):76–86
Ramanathan N, Chellappa R (2006) Modeling age progression in young faces. In: Proceedings of IEEE conference computer vision and pattern recognition, pp 384–394
Rattani A, Reddy N, Derakhshani R (2018) Convolutional neural networks for age classification from smart-phone based ocular images. In: Proceedings of IEEE international joint conference on biometrics (IJCB), pp 756–761
Ricanek K, Tesafaye T (2006) MORPH: a longitudinal image database of normal adult age-progression. In: Proceedings of the IEEE 7th international conference automatic and face gesture recognition, pp 341–345
Riesenhuber M, Poggio T (1999) Hierarchical models of object recognition in cortex. Nat Neurosci 2(11):1019–1025
Robert MH, Shanmugam K, Dinstein I (1973) Texture features for image classification. IEEE Trans System Man Cybernet 3(6):610–621
Rodrguez P, Cucurull G, Gonfaus JM, Roca FX, Gonzlez J (2017) Age and gender recognition in the wild with deep attention. Pattern Recognit 72(C):563–571
Rothe R, Timofte R, Gool LV (2015) DEX: deep expectation of apparent age from a single image. In: International conference on computer vision, ChaLearn looking at people workshop, pp 252–257
Sabharwal T, Gupta R, Son LH, Kumar R, Jha S (2018) Recognition of surgically altered face images: an empirical analysis on recent advances. Artif Intell Rev. https://doi.org/10.1007/s10462-018-9660-0
Sai P, Wang J, Teoh E (2015) Facial age range estimation with extreme learning machines. Neurocomputing 149:364–372
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556
Sun Y, Wang X, Tang X (2013) Deep convolutional network cascade for facial point detection. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 3476–3483
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1–9
Taheri S, Toygar O (2018) Multi-stage age estimation using two level fusions of handcrafted and learned features on facial images. IET Biom 8(2):124–133
Taheri S, Toygar O (2019) On the use of DAG-CNN architecture for age estimation with multi-stage features fusion. Neurocomputing. https://doi.org/10.1016/j.neucom.2018.10.071
Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deepface: closing the gap to human level performance in face verification. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1701–1708
Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650
Thukral P, Mitra K, Chellappa R (2012) A hierarchical approach for human age estimation. In: Proceedings of IEEE international conference on acoustic, speech and signal processing, pp 1529–1532
Triggs B, Dalal N (2005) Histograms of oriented gradients for human detection. In: Proceedings of IEEE on computer vision and pattern recognition, IEEE, San Diego, pp 886–893
Ueki K, Hayashida T, Kobayashi T (2006) Subspace-based age group classification using facial images under various lighting conditions. In: Proceedings of IEEE conference on automatic face and gesture recognition, pp 43–48
Wan J, Tan Z, Lei Z, Guo G, Li SZ (2018) Auxiliary demographic information assisted age estimation with cascaded structure. IEEE Trans Cybernet 48(9):2531–2541
Wang X, Guo R, Kambhamettu C (2015) Deeply-learned feature for age estimation. In: Proceedings of IEEE winter conference on applications of computer vision, pp 534–541
Wu T, Turaga P, Chellappa R (2012) Age estimation and face verification across aging using landmarks. IEEE Trans Inf Forensics Secur 7(6):1780–1788
Yan S, Wang H, Tang X, Huang TS (2007) Learning auto-structured regressor from uncertain non-negative labels. In: Proceedings of IEEE conference on computer vision pp 1–8
Yan S, Xu D, Zhang B, Zhang HJ, Yang Q, Lin S (2007b) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51
Yan S, Liu M, Huang TS (2008) Extracting age information from local spatially flexible patches. In: Proceedings of IEEE conference on acoustics, speech and signal processing, pp 737–740
Yang S and Ramanand D (2015) Multi-scale recognition with DAG-CNNs. In: IEEE international conference on computer vision, pp 1215–1223
Yang M, Zhu S, Lv F, Yu K (2011) Correspondence driven adaptation for human profile recognition. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 505–512
Yang X, Gao BB, Xing C, Huo Z-W, Wei X-S, Zhou Y, Wu J, Geng X (2015) Deep label distribution learning for apparent age estimation. In: IEEE international conference on computer vision workshop, pp 344–350
Yang Y, Chen F, Chen X, Dai Y, Chen Z, Ji J, Zhao T (2016) Video system for human attribute analysis using compact convolutional neural network. In: IEEE international conference on image processing (ICIP), pp 584–588
Yoo B, Kwak Y, Kim Y, Choi C, Kim J (2018) Deep facial age estimation using conditional multitask learning with weak label expansion. IEEE Signal Process Lett 25(6):808–812
Zaghbani S, Boujneh N, Bouhlel MS (2018) Age estimation using deep learning. Comput Electr Eng 68:337–347
Zhang Y, Yeung D (2010) Multi-task warped gaussian process for personalized age estimation. In: IEEE conference on computer vision and pattern recognition (CVPR) pp 2622–2629
Zhuang X, Zhou X, Hasegawa-Johnson M, Huang T (2008) Face age estimation using patch-based hidden Markov model supervectors. In: International conference on image and graphics, pp 1–4
Acknowledgements
Authors would like to express their sincere gratitude to the editor and all the anonymous referees for their valuable comments which have helped to enhance the quality of our research paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Punyani, P., Gupta, R. & Kumar, A. Neural networks for facial age estimation: a survey on recent advances. Artif Intell Rev 53, 3299–3347 (2020). https://doi.org/10.1007/s10462-019-09765-w
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-019-09765-w