Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 8/2016

01.11.2016 | Original Article

Gait recognition and micro-expression recognition based on maximum margin projection with tensor representation

verfasst von: Xianye Ben, Peng Zhang, Rui Yan, Mingqiang Yang, Guodong Ge

Erschienen in: Neural Computing and Applications | Ausgabe 8/2016

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

We contribute, through this paper, to design a novel algorithm called maximum margin projection with tensor representation (MMPTR). This algorithm is able to recognize gait and micro-expression represented as third-order tensors. Through maximizing the inter-class Laplacian scatter and minimizing the intra-class Laplacian scatter, MMPTR can seek a tensor-to-tensor projection that directly extracts discriminative and geometry-preserving features from the original tensorial data. We show the validity of MMPTR through extensive experiments on the CASIA(B) gait database, TUM GAID gait database, and CASME micro-expression database. The proposed MMPTR generally obtains higher accuracy than MPCA, GTDA as well as state-of-the-art DTSA algorithm. Experimental results included in this paper suggest that MMPTR is especially effective in such tensorial object recognition tasks.
Vorheriger Artikel Voice conversion system using salient sub-bands and radial basis function

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Literatur
1.
Daniel E, Lars H, Bernd H (2011) A survey of dimension reduction methods for high-dimensional data analysis and visualization. In: Visualization of large and unstructured data sets, pp 135–149
2.
Andrew RW (1995) Multidimensional scaling by iterative majorization using radial basis functions. Pattern Recogn 28:753–759CrossRef
3.
Tenenbaum JB, Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323CrossRef
4.
Roweis ST, Lawrence KS (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326CrossRef
5.
Kim M, Pavlovic V (2011) Central subspace dimensionality reduction using covariance operators. IEEE Trans Pattern Anal Mach Intell 33:657–670CrossRef
6.
Yang W, Wang Z, Sun C (2015) A collaborative representation based projections method for feature extraction. Pattern Recogn 48:20–27CrossRef
7.
Wang R, Shan S, Chen X, Chen J, Gao W (2011) Maximal linear embedding for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 33:1776–1792CrossRef
8.
He XF, Cai D, Niyogi P (2005) Tensor subspace analysis. In: Advances in neural information proceeding systems 18, Vancouver, Canada
9.
Vasilescu MAO, Terzopoulos D (2005) Multilinear independent components analysis. IEEE Comput Soc Conf Comput Vision Pattern Recogn 1:547–553
10.
Raj RG, Bovik AC (2010) A fast multilinear ICA algorithm. In: 17th IEEE international conference on image processing (ICIP), pp 1889–1892
11.
Lu H, Plataniotis KN, Venetsanopoulos AN (2008) MPCA: multilinear principal component analysis of tensor objects. IEEE Trans Neural Netw 19:18–39CrossRef
12.
Panagakis Y, Kotropoulos C, Arce GR (2010) Non-negative multilinear principal component analysis of auditory temporal modulations for music genre classification. IEEE Trans Audio Speech Lang Process 18:576–588CrossRef
13.
Yan S, Xu D, Yang Q, Zhang L, Tang X, Zhang HJ (2007) Multilinear discriminant analysis for face recognition. IEEE Trans Image Process 16:212–220MathSciNetCrossRef
14.
Yan S, Xu D, Yang Q, Zhang L, Tang X, Zhang HJ (2005) Discriminant analysis with tensor representation. IEEE Comput Soc Conf Comput Vision Pattern Recogn 1:526–532
15.
Tao D, Li X, Wu X, Maybank SJ (2007) General tensor discriminant analysis and gabor features for gait recognition. IEEE Trans Pattern Anal Mach Intell 29:1700–1715CrossRef
16.
17.
Zhang L, Zhang L, Tao D, Huang X (2013) Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction. IEEE Trans Geosci Remote Sens 51:242–256CrossRef
18.
Lu H, Plataniotis KN, Venetsanopoulos AN (2009) Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning. IEEE Trans Neural Netw 20:1820–1836CrossRef
19.
Ben XY, Jiang MY, Yan R et al (2015) Orthogonal multilinear discriminant analysis and its subblock tensor analysis version. Optik Int J Light Electron Opt 126:361–367CrossRef
20.
Lu J, Tan YP (2009) Uncorrelated multilinear geometry preserving projections for multimodal biometrics recognition. In: IEEE international symposium on circuits and systems, pp 2601–2604
21.
Li X, Lin S, Yan S, Xu D (2008) Discriminant locally linear embedding with high-order tensor data. IEEE Trans Syst Man Cybern B Cybern 38:342–352CrossRef
22.
Zheng D, Du X, Cui L (2010) Tensor locality preserving projections for face recognition. In: 2010 IEEE international conference on systems man and cybernetics (SMC), pp 2347–2350
23.
Lu J, Tan YP (2008) Enhanced face recognition using tensor neighborhood preserving discriminant projections. In: 15th IEEE international conference on image processing, pp 1916–1919
24.
Lu J, Wang G, Tan YP (2011) Multilinear locality preserving canonical correlation analysis for face recognition, information. In: 8th international conference on communications and signal processing (ICICS), pp 1–5
25.
Wang SJ, Chen HL, Yan WJ, Chen YH, Fu XL (2014) Face recognition and micro-expression recognition based on discriminant tensor subspace analysis plus extreme learning machine. Neural Process Lett 39(1):25–43CrossRef
26.
Han XH, Chen YW, Ruan X (2012) Multilinear supervised neighborhood embedding of a local descriptor tensor for scene/object recognition. IEEE Trans Image Process 21:1314–1326MathSciNetCrossRef
27.
Liu Y, Liu Y, Chan KCC (2010) Tensor distance based multilinear locality-preserved maximum Information embedding. IEEE Trans Neural Netw 21:1848–1854CrossRef
28.
Liu Y, Liu Y, Chan KCC (2009) Multilinear isometric embedding for visual pattern analysis. In: IEEE 12th international conference on computer vision workshops (ICCV Workshops), pp 212–218
29.
Liu M, Chowdhury AKR (2010) Multilinear feature extraction and classification of multi-focal images, with applications in nematode taxonomy, 2010. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 2823–2830
30.
Feng ZH, Kittler J, Christmas W, Wu XJ, Pfeiffer S (2012) Automatic face annotation by multilinear AAM with missing values. In: 2012 21st international conference on pattern recognition (ICPR), pp 2586–2589
31.
Hu H (2013) Enhanced gabor feature based classification using a regularized locally tensor discriminant model for multiview gait recognition. IEEE Trans Circuits Syst Video Technol 23:1274–1286CrossRef
32.
Ben XY, Xu S, Wang KJ (2012) Review on pedestrian gait feature expression and recognition. Pattern Recog Artif Intell 25:71–81
33.
Yu S, Wang L, Huang K et al. (2004) Gait analysis for human identification in frequency domain. In: Proceedings of the 3rd international conference on image and graphics, pp 282–285
34.
Boulgouris NV, Plataniotis KN, Hatzinakos D (2006) Gait recognition using linear time normalization. Pattern Recogn 39:969–979CrossRefMATH
35.
Chen C, Liang J, Zhu X (2011) Gait recognition based on improved dynamic bayesian networks. Pattern Recogn 44:988–995CrossRef
36.
Wang L, Hu WM, Tan TN (2003) Gait-Based human identification. Chin J Comput 26:353–360
37.
Goffredo M, Carter JN, Nixon MS (2008) Front view gait recognition. In: Proceedings of the 2nd IEEE international conference on biometrics: theory, applications and systems, pp 1–6
38.
Urtasun R, Fua P (2004) 3D tracking for ait characterization and recognition. In: Proceedings of the sixth IEEE international conference on automatic face and gesture recognition, pp 17–22
39.
Ben XY, Xu S, Wang KJ (2012) Research on gait recognition based on trace transform. J Jilin Univ (Engineering and Technology Edition) 42:1–5
40.
Toby HWL, Cheung KH, James NKL (2011) Gait flow image: a silhouette-based gait representation for human identification. Pattern Recogn 44:973–987CrossRefMATH
41.
Wang KJ, Ben XY, Liu LL et al (2009) Gait recognition using information fusion of energy. J Huazhong Univ Sci Technol 37:14–17
42.
Owayjan M, Kashour A, Haddad NA, Fadel M, Souki GA (2012) The design and development of a lie detection system using facial micro-expressions. In: 2012 2nd international conference on advances in computational tools for engineering applications (ACTEA), pp 33–38
43.
Pfister T, Li X, Zhao G, Pietikainen M (2011) Recognising spontaneous facial micro-expressions. In: 2011 IEEE international conference on computer vision (ICCV), pp 1449–1456
44.
Yang W, Wang J, Ren M, Yang J (2009) Feature extraction based on laplacian bidirectional maximum margin criterion. Pattern Recogn 42:2327–2334CrossRefMATH
45.
Yu S, Tan D, Tan T (2006) A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: Proceedings of the 18th international conference on pattern recognition, Hong Kong, China, pp 441–444
46.
Ben XY, Meng WX, Yan R (2012) Dual-ellipse fitting approach for robust gait periodicity detection. Neurocomputing 79:173–178CrossRef
47.
Hofmann M, Geiger J, Bachmann S, Schuller B, Rigoll G (2013) The TUM gait from audio, image and depth (GAID) database: multimodal recognition of subjects and traits. In: Journal of visual communication and image representation, special issue on visual understanding and applications with rgb-d cameras, Elsevier
48.
Hofmann M, Bachmann S, Rigoll G (2012) 2.5D gait biometrics using the depth gradient histogram energy image. In: IEEE fifth international conference on biometrics: theory, applications and systems (BTAS 2012), Washington, DC, USA, pp 23–26
49.
Zhang HL, Ben XY, Zhang P, Liu TJ (2014) Gait period detection based on layered coding of depth information. In: The 2014 international conference on mechanical engineering and intelligent systems, Qingdao, China
50.
Yan WJ, Wu Q, Liu YJ, Wang SJ, Fu XL (2013) CASME database: A dataset of spontaneous micro-expressions collected from neutralized faces. In: 2013 10th IEEE international conference and workshops on automatic face and gesture recognition (FG), pp 1–7
51.
Shen X, Wu Q, Fu X (2012) Effects of the duration of expressions on the recognition of micro-expressions. J Zhejiang Univ Sci B 13:221–230CrossRef
Metadaten
Titel
Gait recognition and micro-expression recognition based on maximum margin projection with tensor representation
verfasst von
Xianye Ben
Peng Zhang
Rui Yan
Mingqiang Yang
Guodong Ge
Publikationsdatum
01.11.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 8/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2031-8

Weitere Artikel der Ausgabe 8/2016

Neural Computing and Applications 8/2016 Zur Ausgabe

Predictive Analytics Using Machine Learning

A fine-grained Random Forests using class decomposition: an application to medical diagnosis

Predictive Analytics Using Machine Learning

A Q-learning-based swarm optimization algorithm for economic dispatch problem

Original Article

Constructing constant mean curvature surfaces with fixed-point half dynamic model

Original Article

A two-stage quantum-behaved particle swarm optimization with skipping search rule and weight to solve continuous optimization problem

Predictive Analytics Using Machine Learning

Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis

Original Article

Prediction of restrained shrinkage crack widths of slag mortar composites by Takagi and Sugeno ANFIS models