Skip to content
2000
  1. Home
  2. A-Z Publications
  3. The Chinese Journal of Artificial Intelligence
  4. Volume 1, Issue 2
  5. Article
Volume 1, Issue 2
  • ISSN: 2666-7827
  • E-ISSN: 2666-7835

Abstract

Background

In computer vision applications, gait-based age estimation across several cameras is critical, especially when following the same person from various viewpoints.

Introduction

Gait-based age recognition is a very challenging task as it involves multiple hurdles, such as a change in the viewpoint of the person. The proposed system handles this problem by performing a sequence of tasks, such as GEI formation from silhouette, applying DCT on GEI and extracting the features and finally using MLP for age estimation. The proposed system proves its effectiveness by comparing the performance with state-of-the-art methods, conventional methods and deep learning-based methods. The performance of the system is estimated on OU-MVLP and OULP-Age datasets. The experimental results show the robustness of the system against viewing angle variations.

Objective

This study aimed to implement the system, which adopts a lightweight approach for gait-based age estimation.

Methods

The proposed system uses a combination of the discrete cosine transform (DCT) and multi-layer perceptron (MLP) on gait energy image (GEI) to perform age estimation.

Results

The performance of the system is extensively evaluated on the OU-MVLP and OULP-Age datasets.

Conclusion

The proposed system attains the best mean absolute error (MAE) of 5.05 (in years) for the OU-MVLP dataset and 5.65 for the OULP dataset.

© 2022 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cjai/10.2174/2666782701666220826104925
2022-09-06
2025-01-31

  • From This Site

    /content/journals/cjai/10.2174/2666782701666220826104925
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. GhoshR. Centre-of-mass based gait recognition for person identification.Rec Adv Comp Sci Commun20211461749175710.2174/2666255813666191119101348
     [Google Scholar]
  2. YuS. TanD. TanT. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition.18th International Conference on Pattern Recognition (ICPR’06)IEEEHong Kong, China 200644144410.1109/ICPR.2006.67
     [Google Scholar]
  3. StevenageS.V. NixonM.S. VinceK. Visual analysis of gait as a cue to identity.Appl. Cogn. Psychol.199913651352610.1002/(SICI)1099‑0720(199912)13:6<513::AID‑ACP616>3.0.CO;2‑8
     [Google Scholar]
  4. MakiharaY. SagawaR. MukaigawaY. EchigoT. YagiY. Gait recognition using a view transformation model in the frequency do-main.Proceedings of the 9th European conference on Computer Vision - Volume Part IIISpringerBerlin, Heidelberg200615116310.1007/11744078_12
     [Google Scholar]
  5. SarkarS. PhillipsP.J. LiuZ. VegaI.R. GrotherP. BowyerK.W. The humanID gait challenge problem: Data sets, performance, and analysis.IEEE Trans. Pattern Anal. Mach. Intell.200527216217710.1109/TPAMI.2005.39 15688555
     [Google Scholar]
  6. LuJ. TanY.P. Ordinary preserving manifold analysis for human age and head pose estimation.IEEE Trans. Hum. Mach. Syst.201343224925810.1109/TSMCC.2012.2192727
     [Google Scholar]
  7. LuJ. TanY.P. Ordinary preserving manifold analysis for human age estimation.IEEE Computer Society and IEEE Biometrics Council Workshop on BiometricsIEEESan Francisco, CA, USA20101610.1109/CVPRW.2010.5544598
     [Google Scholar]
  8. LuJ. TanY.P. Gait-based human age estimation.IEEE Trans. Inf. Forensics Security20105476177010.1109/TIFS.2010.2069560
     [Google Scholar]
  9. DavisJ. Visual categorization of children and adult walking styles.Proceedings of the Third International Conference on Audio and Video Based Biometric Person AuthenticationSpringerBerlin, Heidelberg200129530010.1007/3‑540‑45344‑X_43
     [Google Scholar]
  10. BeggR.K. PalaniswamiM. OwenB. Support vector machines for automated gait classification.IEEE Trans. Biomed. Eng.200552582883810.1109/TBME.2005.845241 15887532
     [Google Scholar]
  11. MakiharaY. OkumuraM. IwamaH. YagiY. Gait-based age estimation using a whole-generation gait database.2011 International Joint Conference on Biometrics (IJCB)IEEEWashington, DC, USA20111610.1109/IJCB.2011.6117531
     [Google Scholar]
  12. XuelongLi Maybank, S.J.; Shuicheng Yan; Dacheng Tao; Dong Xu, Gait components and their application to gender recognition.IEEE Trans. Syst. Man Cybern. C200838214515510.1109/TSMCC.2007.913886
     [Google Scholar]
  13. ShiqiY. TieniuT. KaiqiH. KuiJia Xinyu Wu, A study on gait-based gender classification.IEEE Trans. Image Process.20091881905191010.1109/TIP.2009.2020535 19447706
     [Google Scholar]
  14. LemkeM.R. WendorffT. MiethB. BuhlK. LinnemannM. Spatiotemporal gait patterns during over ground locomotion in major depression compared with healthy controls.J. Psychiatr. Res.2000344-527728310.1016/S0022‑3956(00)00017‑0 11104839
     [Google Scholar]
  15. LiX. MakiharaY. XuC. YagiY. RenM. Gait-based human age estimation using age group-dependent manifold learning and re-gression.Multimedia Tools Appl.20187721283332835410.1007/s11042‑018‑6049‑7
     [Google Scholar]
  16. MannamiH. MakiharaY. YagiY. Gait analysis of gender and age using a large-scale multi-view gait database.Computer Vision - ACCV 2010 - 10th Asian Conference on Computer Vision201097598610.1007/978‑3‑642‑19309‑5_34
     [Google Scholar]
  17. SmolaA.J. SchölkopfB. A tutorial on support vector regression.Stat. Comput.200414319922210.1023/B:STCO.0000035301.49549.88
     [Google Scholar]
  18. BoserB.E. GuyonI.M. VapnikV.N. A discriminant analysis for under sampled data.COLT ’92: Proceedings of the fifth annual workshop on Computational Learning Theory199214415210.1145/130385.130401
     [Google Scholar]
  19. Marn-JimnezM.J. CastroF.M. GuilN. De La TorreF. Medina-CarnicerR. Deep multi-task learning for gait-based biometrics.2017 IEEE International Conference on Image Processing (ICIP)IEEEBeijing, China201710611010.1109/ICIP.2017.8296252
     [Google Scholar]
  20. ZhangS. WangY. LiA. Gait-based age estimation with deep convolutional neural network.2019 International Conference on Biomet-rics (ICB)IEEECrete, Greece20191810.1109/ICB45273.2019.8987240
     [Google Scholar]
  21. LiX. MakiharaY. XuC. YagiY. RenM. Make the bag disappear: Carrying status- invariant gait-based human age estimation using parallel generative adversarial networks.IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS)IEEETampa, FL, USA20191910.1109/BTAS46853.2019.9185973
     [Google Scholar]
  22. XuC. MakiharaY. OgiG. LiX. YagiY. LuJ. The OU-ISIR gait database comprising the large population dataset with age and performance evaluation of age estimation.IPSJ Transactions on Computer Vision and Applications201791242410.1186/s41074‑017‑0035‑2
     [Google Scholar]
  23. SakataA. MakiharaY. TakemuraN. MuramatsuD. YagiY. Gait based age estimation using a dense net.Computer Vision – ACCV 2018 Workshops2018556310.1007/978‑3‑030‑21074‑8_5
     [Google Scholar]
  24. XuC. SakataA. MakiharaY. TakemuraN. MuramatsuD. YagiY. LuJ. Uncertainty-aware gait-based age estimation and its applications.IEEE Trans. Biometrics Behav. Identity Sci.20213447949410.1109/TBIOM.2021.3080300
     [Google Scholar]
  25. XuC. MakiharaY. LiaoR. NiitsumaH. LiX. YagiY. LuJ. Real-time gait-based age estimation and gender classification from a single image.IEEE Winter Conference on Applications of Computer Vision (WACV)IEEEWaikoloa, HI, USA20213459346910.1109/WACV48630.2021.00350
     [Google Scholar]
  26. ChuenB.K.Y. ConnieT. SongO.T. GohM. A preliminary study of gait-based age estimation techniques.Asia-pacific signal and information processing association annual summit and conference (APSIPA)Hong Kong, China, IEEE201580080610.1109/APSIPA.2015.7415382
     [Google Scholar]
  27. NabilaM. MohammedA.I. YousraB.J. Gait‐based human age classification using a silhouette model.IET Biom.20187211612410.1049/iet‑bmt.2016.0176
     [Google Scholar]
  28. MansouriN. AouledI.M. Ben JemaaY. Gait features fusion for efficient automatic age classification.IET Comput. Vis.2018121697510.1049/iet‑cvi.2017.0055
     [Google Scholar]
  29. AbiramiB. SubashiniT.S. MahavaishnaviV. Automatic age-group estimation from gait energy images.Mater. Today Proc.2020334646464910.1016/j.matpr.2020.08.298
     [Google Scholar]
  30. AderinolaT.B. ConnieT. OngT.S. YauW.C. TeohA.B.J. Learning age from gait: A survey.IEEE Access2021910035210036810.1109/ACCESS.2021.3095477
     [Google Scholar]
  31. RiazQ. HashmiM.Z.U.H. HashmiM.A. ShahzadM. ErramiH. WeberA. Move your body: Age estimation based on chest movement during normal walk.IEEE Access20197285102852410.1109/ACCESS.2019.2901959
     [Google Scholar]
  32. PassosW.L. AraujoG.M. GoisJ.N. de LimaA.A. A gait energy image-based system for Brazilian sign language recognition.IEEE Trans. Circuits Syst. I Regul. Pap.202168114761477110.1109/TCSI.2021.3091001
     [Google Scholar]
  33. HanJ. BhanuB. Individual recognition using gait energy image.IEEE Trans. Pattern Anal. Mach. Intell.200628231632210.1109/TPAMI.2006.38 16468626
     [Google Scholar]
  34. AhmedN. NatarajanT. RaoK.R. Discrete cosine transform.IEEE Trans. Comput.1974C-231909310.1109/T‑C.1974.223784
     [Google Scholar]
  35. HemachandranK. Justus RabiB. Performance analysis of discrete cosine transform and discrete wavelet transform for image com-pression.J. Eng. Appl. Sci. (Asian Res. Publ. Netw.)201813243644010.3923/jeasci.2018.436.440
     [Google Scholar]
  36. ChenJ. LiuS. DengG. RahardjaS. Hardware efficient integer discrete cosine transform for efficient image/video compression.IEEE Access2019715263515264510.1109/ACCESS.2019.2947269
     [Google Scholar]
  37. PennebakerW.B. MitchellJ.L. JPEG: Still Image Data Compression Standard.1st edNorwellKluwer Academic Publishers1992
     [Google Scholar]
  38. BentaharA. MeraoumiaA. BendjennaH. IoT securing system using fuzzy commitment for DCT-based fingerprint recognition.3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS)20181510.1109/PAIS.2018.8598511
     [Google Scholar]
  39. AlkhateebJ. RenJ. JiangJ. IpsonS.S. El AbedH. Word-based handwritten Arabic scripts recognition using DCT features and neu-ral network classifier.IEEE Xplore20081510.1109/SSD.2008.4632863
     [Google Scholar]
  40. KohirV.V. DesaiU. Face recognition using a DCT-HMM approach.Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV’98 (Cat. No.98EX201)IEEE, PrincetonNJ, USA199822623110.1109/ACV.1998.732884
     [Google Scholar]
  41. TsaiM.J. HungH.Y. DCT and DWT-based image watermarking by using subsampling.24th International Conference on Distributed Computing Systems Workshops20042004. Proceedings, IEEETokyo, Japan18418910.1109/ICDCSW.2004.1284029
     [Google Scholar]
  42. Al-HajA. Combined DWT-DCT digital image watermarking.J. Comput. Sci.20073974074610.3844/jcssp.2007.740.746
     [Google Scholar]
  43. TakemuraN. MakiharaY. MuramatsuD. EchigoT. YagiY. Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition.IPSJ Trans. Comp. Vis. Appl.20181014410.1186/s41074‑018‑0039‑6
     [Google Scholar]
  44. SakataA. TakemuraN. YagiY. Gait-based age estimation using multi-stage convolutional neural network.IPSJ Trans. Comp. Vis. Appl.20191114410.1186/s41074‑019‑0054‑2
     [Google Scholar]
  45. ShiragaK. MakiharaY. MuramatsuD. EchigoT. YagiY. GEINet: View-invariant gait recognition using a convolutional neural network.International Conference on Biometrics (ICB)IEEEHalmstad, Sweden20161810.1109/ICB.2016.7550060
     [Google Scholar]
  46. YooH.W. KwonK.Y. Method for classification of age and gender using gait recognition.Trans. Korean Soc. Mech. Eng. A.201741111035104510.3795/KSME‑A.2017.41.11.1035
     [Google Scholar]
  47. PunyaniP. GuptaR. KumarA. A comparison study of face, gait and speech features for age estimation.Advances in Electronics, Communication and Computing.1st ed KalamA. DasS. Shar-maK. SingaporeSpringer2018Vol. 44332533110.1007/978‑981‑10‑4765‑7_34
     [Google Scholar]
  48. HemaM. PittaS. Human age classification based on gait parameters using a gait energy image projection model. 3rd International Con-ference on Trends in Electronics and Informatics (ICOEI).Tirunelveli, IndiaIEEE20191163116810.1109/ICOEI.2019.8862788
     [Google Scholar]
  49. AderinolaT.B. ConnieT. OngT.S. GohK.O.M. Automatic extraction of spatio temporal gait features for age group classification.Proceedings of International Conference on Innovations in Information and Communication TechnologiesSpringerSingapore2021717810.1007/978‑981‑16‑0873‑5_6
     [Google Scholar]
/content/journals/cjai/10.2174/2666782701666220826104925
Loading
Robust and Lightweight System for Gait-based Age Estimation towards Viewing Angle Variations
Chin J Arti Intel 1, e260822208023 (2022); https://doi.org/10.2174/2666782701666220826104925
/content/journals/cjai/10.2174/2666782701666220826104925
/content/journals/cjai/10.2174/2666782701666220826104925
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): age estimation; DCT; Gait; GEI; OU-MVLP; OULP

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test