Discriminative aging subspace learning for age estimation

Human age estimation from facial images has become an active research topic in computer vision field because of various real-world applications. Temporal property of facial aging display sequential patterns that lie on the low-dimensional aging manifold. In this paper, we propose hidden factor analy...

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Veröffentlicht in:	Soft computing (Berlin, Germany) Jg. 26; H. 18; S. 9189 - 9198
Hauptverfasser:	Sawant, Manisha, Bhurchandi, Kishor M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022
Schlagworte:	Artificial Intelligence Computational Intelligence Control Data Analytics and Machine Learning Engineering Mathematical Logic and Foundations Mechatronics Robotics Regression Aging manifold Age estimation Hidden factor analysis (HFA)
ISSN:	1432-7643, 1433-7479
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Abstract	Human age estimation from facial images has become an active research topic in computer vision field because of various real-world applications. Temporal property of facial aging display sequential patterns that lie on the low-dimensional aging manifold. In this paper, we propose hidden factor analysis (HFA) model-based discriminative manifold learning method for age estimation. The hidden factor analysis decomposes facial features into independent age factor and identity factor. Various age invariant face recognition systems in the literature utilize identity factor for face recognition; however, the age factor remains unutilized. The age component of the hidden factor analysis model depends on the subject’s age. Thus it carries significant age-related information. In this paper, we demonstrate that such aging patterns can be effectively extracted from the HFA-based discriminant subspace learning algorithm. Next, we have applied multiple regression methods on low-dimensional aging features learned from the HFA model. Effect of reduced dimensionality on the accuracy has been evaluated by extensive experiments and compared with the state-of-the-art methods. Effectiveness and robustness in terms of MAE and CS of the proposed framework are demonstrated using experimental analysis on a large-scale aging database MORPH II. The accuracy of our method is found superior to the current state-of-the-art methods.
AbstractList	Human age estimation from facial images has become an active research topic in computer vision field because of various real-world applications. Temporal property of facial aging display sequential patterns that lie on the low-dimensional aging manifold. In this paper, we propose hidden factor analysis (HFA) model-based discriminative manifold learning method for age estimation. The hidden factor analysis decomposes facial features into independent age factor and identity factor. Various age invariant face recognition systems in the literature utilize identity factor for face recognition; however, the age factor remains unutilized. The age component of the hidden factor analysis model depends on the subject’s age. Thus it carries significant age-related information. In this paper, we demonstrate that such aging patterns can be effectively extracted from the HFA-based discriminant subspace learning algorithm. Next, we have applied multiple regression methods on low-dimensional aging features learned from the HFA model. Effect of reduced dimensionality on the accuracy has been evaluated by extensive experiments and compared with the state-of-the-art methods. Effectiveness and robustness in terms of MAE and CS of the proposed framework are demonstrated using experimental analysis on a large-scale aging database MORPH II. The accuracy of our method is found superior to the current state-of-the-art methods.
Author	Sawant, Manisha Bhurchandi, Kishor M.
Author_xml	– sequence: 1 givenname: Manisha orcidid: 0000-0002-3682-4146 surname: Sawant fullname: Sawant, Manisha email: mpparlewar@gmail.com organization: Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology – sequence: 2 givenname: Kishor M. surname: Bhurchandi fullname: Bhurchandi, Kishor M. organization: Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology
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IEEE AbualigahLDiabatAMirjaliliSAbd ElazizMGandomiAHThe arithmetic optimization algorithmComput Methods Appl Mech Eng2021376419929910.1016/j.cma.2020.113609 BelkinMNiyogiPLaplacian eigenmaps for dimensionality reduction and data representationNeural Comput20031561373139610.1162/089976603321780317 L Abualigah (7333_CR4) 2022; 191 L Abualigah (7333_CR3) 2021; 157 WL Chao (7333_CR10) 2013; 46 G Guo (7333_CR18) 2008; 17 MTB Iqbal (7333_CR26) 2017; 12 7333_CR48 7333_CR47 7333_CR46 7333_CR45 7333_CR44 7333_CR43 7333_CR42 7333_CR51 7333_CR50 Y Fu (7333_CR13) 2008; 10 CC Chang (7333_CR9) 2011; 2 M Belkin (7333_CR7) 2003; 15 7333_CR19 7333_CR17 7333_CR16 7333_CR12 X Geng (7333_CR14) 2007; 29 SJ Prince (7333_CR36) 2008; 30 X Geng (7333_CR15) 2013; 35 H Li (7333_CR28) 2017; 24 H Han (7333_CR22) 2015; 37 TK Moon (7333_CR33) 1996; 13 7333_CR29 I Huerta (7333_CR24) 2015; 68 JB Tenenbaum (7333_CR41) 2000; 290 7333_CR25 7333_CR23 7333_CR21 7333_CR1 7333_CR20 7333_CR30 C Xu (7333_CR49) 2017; 222 7333_CR6 TF Cootes (7333_CR11) 2001; 23 7333_CR8 M Sawant (7333_CR40) 2019; 78 JK Pontes (7333_CR34) 2016; 54 J Lu (7333_CR32) 2013; 43 T Ahonen (7333_CR5) 2006; 28 7333_CR38 M Sawant (7333_CR39) 2019; 7 7333_CR37 L Abualigah (7333_CR2) 2021; 376 A Lanitis (7333_CR27) 2002; 24 7333_CR35 7333_CR31
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Mathematical Probl Eng 2014 – reference: LiHZouHHuHModified hidden factor analysis for cross-age face recognitionIEEE Signal Process Lett201724446546910.1109/LSP.2017.2661983 – reference: Wang J, Shang Y, Su G, Lin X (2006) Age simulation for face recognition. In: Pattern recognition, 2006. ICPR 2006. 18th international conference on, vol. 3, pp 913–916. IEEE – reference: XuCLiuQYeMAge invariant face recognition and retrieval by coupled auto-encoder networksNeurocomputing2017222627110.1016/j.neucom.2016.10.010 – reference: Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol 1, pp 886–893. IEEE – reference: Zhu K, Gong D, Li Z, Tang X (2014) Orthogonal gaussian process for automatic age estimation. In: Proceedings of the 22nd ACM international conference on multimedia, pp 857–860. ACM – reference: Guo G, Mu G (2013) Joint estimation of age, gender and ethnicity: Cca versus pls. In: 2013 10th IEEE international conference and workshops on automatic face and gesture recognition (FG), pp 1–6. IEEE – reference: LanitisATaylorCJCootesTFToward automatic simulation of aging effects on face imagesIEEE Transact Pattern Anal Mach Intell200224444245510.1109/34.993553 – reference: GengXYinCZhouZHFacial age estimation by learning from label distributionsIEEE Transact Pattern Anal Mach Intell201335102401241210.1109/TPAMI.2013.51 – reference: SawantMBhurchandiKHierarchical facial age estimation using gaussian process regressionIEEE Access201979142915210.1109/ACCESS.2018.2889873 – reference: Huo Z, Yang X, Xing C, Zhou Y, Hou P, Lv J, Geng X (2016) Deep age distribution learning for apparent age estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 17–24 – reference: CootesTFEdwardsGJTaylorCJActive appearance modelsIEEE Transact Pattern Anal Mach Intell200123668168510.1109/34.927467 – reference: LuJTanYPOrdinary preserving manifold analysis for human age and head pose estimationIEEE Transact Human-Machine Syst201343224925810.1109/TSMCC.2012.2192727 – reference: IqbalMTBShoyaibMRyuBAbdullah-Al-WadudMChaeODirectional age-primitive pattern (dapp) for human age group recognition and age estimationIEEE Transact Inf Forensics Secur201712112505251710.1109/TIFS.2017.2695456 – 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 – reference: Weng R, Lu J, Yang G, Tan YP (2013) Multi-feature ordinal ranking for facial age estimation. 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Snippet	Human age estimation from facial images has become an active research topic in computer vision field because of various real-world applications. Temporal...
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SubjectTerms	Artificial Intelligence Computational Intelligence Control Data Analytics and Machine Learning Engineering Mathematical Logic and Foundations Mechatronics Robotics
Title	Discriminative aging subspace learning for age estimation
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