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
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 6/2014

01.12.2014 | Original Article

Human facial expression analysis based on image granule LPP

verfasst von: Xiuxiu Xu, Jiuzhen Liang, Sisi Lv, Qin Wu

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 6/2014

Einloggen

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

search-config
loading …

Abstract

This paper proposes a synthetic technique for human facial expression analysis based on locality preserving projections (LPP) and granular computing. The proposed method decreases the computational complexity of LPP and preserves the performance of LPP algorithm. In image processing, an image can be divided into various sizes of blocks, which are defined as image granules in this paper. The LPP algorithm is implemented to images with various image granules. By this strategy, the image dimension reduces quickly, which decreases the computational complexity. The experiments on three face databases are presented to show the performance of image granule LPP. Distribution of the facial pose and expression is shown and the computational complexities with different image granules are compared. Meanwhile, the order-preserving property of images is investigated by tracking the sequence of designated images. And the loss of image information is analyzed by image roughness, entropy and histogram. Furthermore, the parameter setting in LPP is discussed because of its non-ignorable affect on the experiment results. Finally, the method is applied to facial expression recognition.
Vorheriger Artikel Towards enriching the quality of k-nearest neighbor rule for document classification
Nächster Artikel Reduced-reference image quality assessment through SIFT intensity ratio

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

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

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"

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
Weitere Produktempfehlungen anzeigen
Literatur
1.
Li SZ, Jain AK (2005) Handbook of face recognition. Springer, New YorkMATH
2.
Turk MA, Pentland AP (1991) Face recognition using eigenfaces. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Maui, HI, USA, pp 586–591
3.
Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRef
4.
Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 13(6):1450–1464CrossRef
5.
6.
7.
Poon B, Amin MA, Yan H (2011) Performance evaluation and comparison of PCA based human face recognition methods for distorted images. Int J Mach Learn Cybern 2(4):245–259CrossRef
8.
Zhao H, Yao L (2013) Face hallucination using example-based regularization. Int J Mach Learn Cybern 4(6):693–701CrossRefMathSciNet
9.
Xu X, Liu W, Venkatesh S (2012) An innovative face image enhancement based on principle component analysis[J]. Int J Mach Learn Cybern 3(4):259–267CrossRef
10.
Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326CrossRef
11.
Sam Roweis LS, Hinton G (2001) Global coordination of local linear models. Adv Neural Inf Process Syst 14:889–896
12.
Tenenbaum J, de Silva V, Langford J (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(12):2319–2323CrossRef
13.
Chang Y, Hu C, Turk M (2003) Manifold of facial expression. In: Proceedings of IEEE International Workshop on Analysis and Modeling of Faces and Gestures, Nice, France
14.
Lee K-C, Ho J, Yang M-H, Kriegman D (2003) Video-based face recognition using probabilistic appearance manifolds. In: IEEE Conference on Computer Vision and Pattern Recognition
15.
Seung HS, Lee DD (2000) The manifold ways of perception. Science 290:2268–2269
16.
Shashua A, Levin A, Avidan S (2002) Manifold pursuit: a new approach to appearance based recognition. In: Proceedings of 16th International Conference on Pattern Recognition 3, pp 590–594
17.
Bernstein M, De Silva V, Langford JC, Tenenbaum JB (2000) Graph approximations to geodesics on embedded manifolds, Technical report, Department of Psychology, Stanford University
18.
Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396CrossRefMATH
19.
Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems, vol 14, pp 589–591
20.
He X, Niyogi P (2003) Locality preserving projections. In: Proceedings of the Conference on Neural Information Processing Systems. Vancouver, Canada, pp 153–160
21.
He X, Yan S, Hu Y, Niyogi P, Zhang HJ (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340CrossRef
22.
Chung FRK (1997) Spectral graph theory. Regional conference series in mathematics, vol 92
23.
Yan S, Xu D, Zhang B, Zhang H (2005) Graph embedding: a general framework for dimensionality reduction. CVPR 2:830–837
24.
Yang J, Zhang D, Yang JY, Niu B (2007) Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans Pattern Anal Mach Intell 29(4):650–664CrossRef
25.
He X, Yan S, Hu Y, Zhang H (2003) Learning a locality preserving subspace for visual recognition. In: ICCV 2003, pp 385–393
26.
Lin TY (1997) Granular computing: from rough sets and neighborhood system to information granulation and computing in words. In: European Congress on Intelligent Techniques and Soft Computing
27.
Butenkov SA (2004) Granular computing in image processing and understanding. In: Proceedings of IASTED International Conference on Artificial Intelligence and Applications. Innsbruck, Austria
28.
Pal SK, Uma Shankar B, Mitra P (2005) Granular computing, rough entropy and object extraction. Pattern Recogn Lett 26(16):2509–2517CrossRef
29.
Butenkov SA, Krivsha VV, Saud AD (2006) Granular computing in computer image perception: basic issues and glass box models. In: Proceedings of the 24th IASTED International Conference on Artificial Intelligence and Applications, pp 462–467
30.
Bargiela A, Pedrycz W (2008) Toward a theory of granular computing for human-centered information processing. IEEE Trans Fuzzy Syst 16:320–330CrossRef
31.
Bhatt HS, Samarth B, Singh R, Vatsa M, Noore A (2011) Evolutionary granular approach for recognizing faces altered due to plastic surgery. In ‘FG’, IEEE, pp 720–725
32.
Chung FRK (1997) Spectral graph theory, volume 92 of Regional Conference Series in Mathematics
33.
Skočal D, Bischof H, Leonardis A (2002) A robust PCA algorithm for building representations from panoramic images. European Conference on Computer Vision, vol IV. Springer, New York, pp 761–775
34.
Zadeh LA (1997) Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111–127CrossRefMATHMathSciNet
35.
Zadeh LA (1998) Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems. Soft Comput 2(1):23–25CrossRef
36.
Lin TY (1998) Granular computing on binary relations in data mining and neighborhood systems. In: Proceedings of Rough Sets in Knowledge Discovery, Physica-Verlag, Heidelberg, pp 107–120
37.
Yao JT, Vasilakos AV, Pedrycz W (2013) Granular computing: perspectives and challenges. IEEE Trans Cybern 99:1–13
38.
Zhang B, Zhang L (2010) Discuss on future development of granular computing. J Chongqing Univ Posts Telecommun (Natural Science Edition) 22(5):538–540
39.
Zhou G (2006) Granular computing model and its application. Master Degree Dissertation, Zhejiang Normal University
40.
Tian Y, Kanade T, Cohn JF (2005) Facial expression recognition. In: Li SZ, Jain AK (eds) Handbook of face recognition. Springer, New York
41.
Shan C, Gong S, McOwan PW (2006) A comprehensive empirical study on linear subspace methods for facial expression analysis. In: Conference on Vision and Pattern Recognition Workshop (CVPRW ’06)
Metadaten
Titel
Human facial expression analysis based on image granule LPP
verfasst von
Xiuxiu Xu
Jiuzhen Liang
Sisi Lv
Qin Wu
Publikationsdatum
01.12.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 6/2014
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0228-2

Weitere Artikel der Ausgabe 6/2014

International Journal of Machine Learning and Cybernetics 6/2014 Zur Ausgabe

Original Article

Dynamic updating multigranulation fuzzy rough set: approximations and reducts

Original Article

Reduced-reference image quality assessment through SIFT intensity ratio

Original Article

Conditions for coverings to induce matroids

Original Article

Efficient mining frequent itemsets algorithms

Original Article

Fuzzy clustering with non-local information for image segmentation

Original Article

A comparison of ℓ1-regularizion, PCA, KPCA and ICA for dimensionality reduction in logistic regression