Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Pattern Analysis and Applications
Published in: Pattern Analysis and Applications 4/2019

11-02-2019 | Industrial and commercial application

Gait-based person re-identification under covariate factors

Authors: Emna Fendri, Imen Chtourou, Mohamed Hammami

Published in: Pattern Analysis and Applications | Issue 4/2019

Log in

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

search-config
loading …

Abstract

Gait is recognized as an effective behavioral biometric trait. Gait pattern information can be captured and perceived from a distance thanks to its noninvasive and less intrusive nature. Therefore, gait could be well suited for person re-identification. However, semantic information like clothing and carrying bags has a remarkable influence on its accuracy. Unlike the existing solutions, this paper proposed a new method for gait-based person re-identification relying on dynamic selection of human parts. This method consists in computing a new person descriptor from relevant selected human parts. The selection of the most informative parts was achieved depending on the presence of semantic information. Our experiments were performed on the CASIA-B database revealing promising results and showing the effectiveness of the proposed method.
previous article Neuro-probabilistic model for object tracking
next article Contour recognition of roadheader cutting head based on shape matching

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!

inform now

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!

inform now

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!

inform now
Literature
1.
Alotaibi M, Mahmood A (2017) Reducing covariate factors of gait recognition using feature selection and dictionary-based sparse coding. Signal Image Video Process 11(6):1131–1138CrossRef
2.
An L, Chen X, Kafai M, Yang S, Bhanu B (2013) Improving person re-identification by soft biometrics based reranking. In: 2013 7th international conference on distributed smart cameras (ICDSC). IEEE, pp 1–6
3.
Arora P, Srivastava S et al (2016) Human gait recognition using gait flow image and extension neural network. In: Proceedings of the 2nd international conference on computer and communication technologies. Springer, Berlin, pp 1–10
4.
Bashir K, Xiang T, Gong S (2010) Gait recognition without subject cooperation. Pattern Recognit Lett 31(13):2052–2060CrossRef
5.
Bedagkar-Gala A, Shah SK (2014) Gait-assisted person re-identification in wide area surveillance. In: Asian conference on computer vision. Springer, Berlin, pp 633–649
6.
Benouis M, Senouci M, Tlemsani R, Mostefai L (2016) Gait recognition based on model-based methods and deep belief networks. Int J Biomet 8(3–4):237–253CrossRef
7.
Binsaadoon AG, El-Alfy ESM (2016) Gait-based recognition for human identification using fuzzy local binary patterns. In: ICAART (2), pp 314–321
8.
Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the 5th annual workshop on computational learning theory. ACM, pp 144–152
9.
Chapelle O, Keerthi SS (2010) Efficient algorithms for ranking with svms. Inf Retr 13(3):201–215CrossRef
10.
Chen C, Liang J, Zhao H, Hu H, Tian J (2009) Frame difference energy image for gait recognition with incomplete silhouettes. Pattern Recognit Lett 30(11):977–984CrossRef
11.
Choudhury SD, Tjahjadi T (2015) Robust view-invariant multiscale gait recognition. Pattern Recognit 48(3):798–811CrossRef
12.
Cunado D, Nixon MS, Carter JN (2003) Automatic extraction and description of human gait models for recognition purposes. Comput Vis Image Underst 90(1):1–41CrossRef
13.
Dempster WT, Gaughran GR (1967) Properties of body segments based on size and weight. Dev Dyn 120(1):33–54
14.
Dupuis Y, Savatier X, Vasseur P (2013) Feature subset selection applied to model-free gait recognition. Image Vis Comput 31(8):580–591CrossRef
15.
Gabriel-Sanz S, Vera-Rodriguez R, Tome P, Fierrez J (2013) Assessment of gait recognition based on the lower part of the human body. In: 2013 international workshop on biometrics and forensics (IWBF). IEEE, pp 1–4
16.
Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensemble of localized features. Comput Vis ECCV 2008:262–275
17.
Gu J, Ding X, Wang S, Wu Y (2010) Action and gait recognition from recovered 3-D human joints. IEEE Trans Syst Man Cybern Part B (Cybern) 40(4):1021–1033CrossRef
18.
Hosseini NK, Nordin MJ (2013) Human gait recognition: a silhouette based approach. J Autom Control Eng 1(2):259–267
19.
Iwama H, Okumura M, Makihara Y, Yagi Y (2012) The ou-isir gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Trans Inf Forensics Secur 7(5):1511–1521CrossRef
20.
Iwashita Y, Uchino K, Kurazume R (2013) Gait-based person identification robust to changes in appearance. Sensors 13(6):7884–7901CrossRef
21.
Khalid B, Tao X, Shaogang G (2009) Gait recognition using gait entropy image. In: 3rd international conference on crime detection and prevention (ICDP 2009)
22.
Khedher MI (2014) Ré-identification de personnes à partir des séquences vidéo. PhD thesis, Institut National des Télécommunications
23.
Kovač J, Peer P (2014) Human skeleton model based dynamic features for walking speed invariant gait recognition. Math Probl Eng 2014:15CrossRef
24.
Kumar HM, Nagendraswamy H (2014) LBP for gait recognition: a symbolic approach based on GEI plus RBL of GEI. In: 2014 international conference on electronics and communication systems (ICECS). IEEE, pp 1–5
25.
Kusakunniran W (2014) Attribute-based learning for gait recognition using spatio-temporal interest points. Image Vis Comput 32(12):1117–1126CrossRef
26.
Kusakunniran W, Wu Q, Li H, Zhang J (2009) Automatic gait recognition using weighted binary pattern on video. In: 6th IEEE international conference on advanced video and signal based surveillance, 2009. AVSS’09. IEEE, pp 49–54
27.
Lam TH, Cheung KH, Liu JN (2011) Gait flow image: a silhouette-based gait representation for human identification. Pattern Recognit 44(4):973–987MATHCrossRef
28.
Layne R, Hospedales TM, Gong S (2014) Attributes-based re-identification. In: Person re-identification. Springer, Berlin, pp 93–117
29.
Lee CP, Tan AW, Tan SC (2015) Gait recognition with transient binary patterns. J Vis Commun Image Represent 33:69–77CrossRef
30.
Li N, Xu Y, Yang XK (2010) Part-based human gait identification under clothing and carrying condition variations. In: 2010 international conference on machine learning and cybernetics (ICMLC), vol 1. IEEE, pp 268–273
31.
Li X, Chen Y (2013) Gait recognition based on structural gait energy image. J Comput Inf Syst 9(1):121–126
32.
Liang Y, Li CT, Guan Y, Hu Y (2016) Gait recognition based on the golden ratio. EURASIP J Image Video Process 2016(1):22CrossRef
33.
Lishani AO, Boubchir L, Khalifa E, Bouridane A (2017) Human gait recognition based on Haralick features. Signal Image Video Process 11:1–8CrossRef
34.
Liu D, Ye M, Li X, Zhang F, Lin, L.: Memory-based gait recognition. In: BMVC (2016)
35.
Liu W, Liu H, Tao D, Wang Y, Lu K (2015) Multiview hessian regularized logistic regression for action recognition. Signal Process 110:101–107CrossRef
36.
Liu W, Zha ZJ, Wang Y, Lu K, Tao D (2016) \(p\)-laplacian regularized sparse coding for human activity recognition. IEEE Trans Ind Electron 63(8):5120–5129
37.
Liu Y, Zhang J, Wang C, Wang L (2012) Multiple hog templates for gait recognition. In: 2012 21st international conference on pattern recognition (ICPR). IEEE, pp 2930–2933
38.
Liu Z, Zhang Z, Wu Q, Wang Y (2015) Enhancing person re-identification by integrating gait biometric. Neurocomputing 168:1144–1156CrossRef
39.
Man J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28(2):316–322CrossRef
40.
Martín-Félez R, Xiang T (2014) Uncooperative gait recognition by learning to rank. Pattern Recognit 47(12):3793–3806CrossRef
41.
Nandy A, Pathak A, Chakraborty P (2017) A study on gait entropy image analysis for clothing invariant human identification. Multimed Tools Appl 76(7):9133–9167CrossRef
42.
Nixon M et al (2009) Model-based gait recognition. Encyclopedia of biometrics. Springer, Heidelberg, pp 633–639
43.
Prosser BJ, Zheng WS, Gong S, Xiang T, Mary Q (2010) Person re-identification by support vector ranking. In: BMVC, vol 2, p 6 (2010)
44.
Rafi M, Khammari H, Wahidabanu R, Taj Y (2013) A model based approach for gait recognition system. Int J Soft Comput Eng (IJSCE) 3:2231–2307
45.
Rida I, Almaadeed S, Bouridane A (2016) Gait recognition based on modified phase-only correlation. Signal Image Video Process 10(3):463–470CrossRef
46.
Rumelhart DE, Hinton GE, Williams RJ et al (1998) Learning representations by back-propagating errors. Cognit Model 5(3):1MATH
47.
Saadoon A, Nordin MJ (2015) An automatic human gait recognition system based on joint angle estimation on silhouette images. J Theor Appl Inf Technol 81(2):277
48.
Sarkar S, Phillips PJ, Liu Z, Vega IR, Grother P, Bowyer KW (2005) The humanid gait challenge problem: data sets, performance, and analysis. IEEE Trans Pattern Anal Mach Intell 27(2):162–177CrossRef
49.
Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge
50.
Sivapalan S, Chen D, Denman S, Sridharan S, Fookes C (2013) Histogram of weighted local directions for gait recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 125–130
51.
Tafazzoli F, Safabakhsh R (2010) Model-based human gait recognition using leg and arm movements. Eng Appl Artif Intell 23(8):1237–1246CrossRef
52.
Wang C, Zhang J, Pu J, Yuan X, Wang L (2010) Chrono-gait image: a novel temporal template for gait recognition. Comput Vis ECCV 2010:257–270
53.
Wei L, Tian Y, Wang Y, Huang T (2015) Swiss-system based cascade ranking for gait-based person re-identification. In: AAAI, pp 1882–1888
54.
Yamauchi K, Bhanu B, Saito H (2009) Recognition of walking humans in 3D: initial results. In: 2009 CVPR Workshops 2009. IEEE computer society conference on computer vision and pattern recognition workshops. IEEE, pp 45–52
55.
Yang X, Liu W, Tao D, Cheng J (2017) Canonical correlation analysis networks for two-view image recognition. Inf Sci 385:338–352CrossRef
56.
Yegnanarayana B (2009) Artificial neural networks. PHI Learning Pvt. Ltd, New Delhi
57.
Zeng W, Wang C, Li Y (2014) Model-based human gait recognition via deterministic learning. Cognit Comput 6(2):218–229CrossRef
58.
Yu S, Tan D, Tan T (2006) A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th international conference on pattern recognition, ICPR 2006, vol 4, pp 441–444
59.
Zighed DA, Rakotomalala R (2000) Graphes d’induction: apprentissage et data mining. Hermes Paris, Paris
Metadata
Title
Gait-based person re-identification under covariate factors
Authors
Emna Fendri
Imen Chtourou
Mohamed Hammami
Publication date
11-02-2019
Publisher
Springer London
Published in
Pattern Analysis and Applications / Issue 4/2019
Print ISSN: 1433-7541
Electronic ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-019-00793-4

Other articles of this Issue 4/2019

Pattern Analysis and Applications 4/2019 Go to the issue

Industrial and commercial application

Multi-channel local ternary pattern for content-based image retrieval

Short Paper

A hybrid cascade method for EEG classification

Theoretical Advances

Online updating of active function cross-entropy clustering

Short paper

Hybrid hidden Markov models and artificial neural networks for handwritten music recognition in mensural notation

Theoretical Advances

Relevant feature selection in the context of painting classification

Theoretical Advances

An implementation of optimized framework for action classification using multilayers neural network on selected fused features

Premium Partner

    Image Credits