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
International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 8/2018

19.03.2017 | Original Article

Local descriptor margin projections (LDMP) for face recognition

verfasst von: Zhangjing Yang, Pu Huang, Minghua Wan, Fanlong Zhang, Guowei Yang, Chengshan Qian, Jincheng Zhang, Zuoyong Li

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 8/2018

Einloggen

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

search-config
loading …

Abstract

Feature extraction is a key problem in face recognition systems. This paper tackles this problem by combining the strength of image descriptor with dimensionality reduction technology. So, this paper proposes a new efficient face recognition method-local descriptor margin projections (LDMP). Firstly, we propose a novel local descriptor for face image representation. At this step, an effective and simple metric approach named gray value accumulating distance (GAD) is firstly proposed. And then a novel local descriptor based on GAD is presented to capture the local structure information between central pixel and its neighbors effectively. Secondly, we propose a dimensionality reduction algorithm named maximum margin learning projections (MMLP) which can obtain the low-dimensional and discriminative feature. Finally, experimental results on the Yale, Extended Yale B, PIE, AR and LFW face databases show the effectiveness of the proposed method.
Vorheriger Artikel Robust filtering for uncertain discrete-time stochastic neural networks with Markovian jump and mixed time-delays
Nächster Artikel A survey on application of machine learning for Internet of Things

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
Fußnoten
Literatur
1.
Zhao WY, Chellappa R, Phillips PJ et al (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–459CrossRef
2.
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86CrossRef
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.
Yuan C, Sun X, Rui L (2016) Fingerprint liveness detection based on multi-scale LPQ and PCA. China Communications. 13(7):60–65
5.
6.
Yan SC, Xu D, Zhang BY et al (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51CrossRef
7.
Tenenbaum JB, Silva VD, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323CrossRef
8.
Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326CrossRef
9.
He XF, Yan SC, Hu YX et al (2003) Learning a locality preserving subspace for visual recognition. In: Proceedings of the 9th IEEE International Conference on Computer Vision, pp 385–392
10.
He XF, Yan SC, Hu YX et al (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340CrossRef
11.
Lai ZH, Wong WK, Xu Y et al (2015) Approximate orthogonal sparse embedding for dimensionality reduction. IEEE Trans Neural Netw Learn Syst 27(4):723–735MathSciNetCrossRef
12.
Yang M, Zhu P, Liu F et al (2015) Joint representation and pattern learning for robust face recognition. Neurocomputing 168:70–80CrossRef
13.
Huang P, Chen CK, Tang ZM et al (2014) Discriminant similarity and variance preserving projection for feature extraction. Neurocomputing 139:180–188CrossRef
14.
Lai ZH, Xu Y, Jin Z et al (2014) Human gait recognition via sparse discriminant projection learning. IEEE Trans Circuits Syst Video Technol 24(10):1651–1662CrossRef
15.
Shi X, Guo Z, Lai ZH (2014) Face recognition by sparse discriminant analysis via joint L2,1-norm minimization. Pattern Recognit 47(7):2447–2453CrossRef
16.
Huang P, Chen CK, Tang ZM et al (2014) Local maximal marginal discriminant embedding for face recognition. J Visual Commun Image Represent 25(2):296–305CrossRef
17.
Chen Y, Xu XH (2014) Supervised orthogonal discriminant subspace projects learning for face recognition. Neural Netw 50:33–36CrossRefMATH
18.
Huang P, Chen CK, Tang ZM et al (2014) Feature extraction using local structure preserving discriminant analysis. Neurocomputing 140:104–113CrossRef
19.
Wan MH, Li M, Yang GW et al (2014) Feature extraction using two-dimensional maximum embedding difference. Inf Sci 274:55–69CrossRef
20.
Cai D, He XF, Zhou K et al (2007) Locality sensitive discriminant analysis. In: Proceedings of the IEEE International Conference on Artificial Intelligence, pp 708–713
21.
Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041CrossRefMATH
22.
Zhang BC, Gao YS, Zhao SQ et al (2010) Local derivative pattern versus local binary pattern:face recognition with high-order local pattern descriptor. IEEE Trans Image Process 19(2):533–544MathSciNetCrossRefMATH
23.
Zhang WC, Shan SG, Gao W et al (2005) Local Gabor binary pattern histogram sequence (LGBPHS):a novel non-statistical model for face representation and recognition. In: Proceedings of the 10th IEEE International Conference on Computer Vision, pp 786–791
24.
Tan XY, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650MathSciNetCrossRefMATH
25.
[25] Wolf L, Hassner T, Taigman Y (2011) Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE Trans Pattern Anal Mach Intell 33(10):1978–1990CrossRef
26.
Liu CJ, Wechsler H (2002) Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Trans Image Process 11(4):467–476CrossRef
27.
Pang Y, Yuan Y, Li X (2008) Gabor-based region covariance matrices for face recognition. IEEE Trans Circuits Syst Video Technol 18(7):989–993CrossRef
28.
Zhang BC, Shan SG, Chen XL et al (2007) Histogram of gabor phase patterns (HGPP): a novel object representation approach for face recognition. IEEE Trans Image Process 16(1):57–68MathSciNetCrossRef
29.
Lei Z, Liao SC, Pietikainen M et al (2011) Face recognition by exploring information jointly in space, scale and orientation. IEEE Trans Image Process 20(1):247–256MathSciNetCrossRef
30.
Gai S, Yang GW, Wan MH (2013) Employing quaternion wavelet transform for banknote classification. Neurocompuing 118(8):171–178CrossRef
31.
Gu B, Sheng VS, Tay KY et al (2014) Incremental support vector learning for ordinal regression[J]. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416MathSciNetCrossRef
32.
Wen X, Shao L, Xue Y et al (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406CrossRef
33.
34.
Qian JJ, Yang J, Gao GW (2013) Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction. Pattern Recognit 46(10):2724–2739CrossRef
35.
Nouyed I, Poon B, Amin MA et al (2015) A study on the discriminating characteristics of Gabor phase-face and an improved method for face recognition. Int J Mach Learn Cyber 7(6):1115–1130CrossRef
36.
Lai ZH, Xu Y, Chen QC et al (2014) Multilinear Sparse Principal Component Analysis. IEEE Trans Neural Netw Learn Syst 25(10):1942–1950CrossRef
37.
Lai ZH, Wong WK, Xu Y et al (2014) Sparse alignment for robust tensor learning. IEEE Trans Neural Netw Learn Syst 25(10):1779–1792CrossRef
38.
Seo HJ, Milanfar P (2010) Training-Free, generic object detection using locally adaptive regression kernels. IEEE Trans Pattern Anal Mach Intell 32(9):1688–1704CrossRef
39.
[39] Seo HJ, Milanfar P (2011) Face verification using the LARK representation. IEEE Trans Inf Forensics Secur 6(4):1275–1286CrossRef
40.
Liao SC, Zhu XX, Lei Z et al (2007) Learning multi-scale block local binary patterns for face recognition. In: Proceedings of the IEEE International Conference on Biometrics vol 4642, pp 828–837
41.
Qian JJ, Yang J, Xu Y (2013) Local structure-based image decomposition for feature extraction with applications to face recognition. IEEE Trans Image Process 22(9):3591–3603CrossRef
42.
Sugiyama M (2007) Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J Mach Learn Res 8(5):1027–1061MATH
43.
Lee KC, Ho J, Kriegman DJ (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698CrossRef
44.
Gross R, Matthews I, Cohn J et al (2007) The cmu multi-pose, illumination, and expression (multi-pie) face database. Technical report #07–08. Carnegie Mellon University Robotics Institute
45.
Martinez AM, Benavente R (1998) The AR face database. CVC Technical Report #24
46.
Huang GB, Ramesh M, Berg T et al (2007) Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report# 07–49. University of Massachusetts, Amherst
47.
Wolf L, Hassner T, Taigman Y (2009) Similarity scores based on background samples. Computer Vision—ACCV 2009. Springer, Berlin, pp 88–97
Metadaten
Titel
Local descriptor margin projections (LDMP) for face recognition
verfasst von
Zhangjing Yang
Pu Huang
Minghua Wan
Fanlong Zhang
Guowei Yang
Chengshan Qian
Jincheng Zhang
Zuoyong Li
Publikationsdatum
19.03.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 8/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0652-1

Weitere Artikel der Ausgabe 8/2018

International Journal of Machine Learning and Cybernetics 8/2018 Zur Ausgabe

Original Article

Two swarm intelligence approaches for tuning extreme learning machine

Original Article

Adaptive neural nonsingular terminal sliding mode control for MEMS gyroscope based on dynamic surface controller

Original Article

Limit cycle oscillation in aeroelastic systems and its adaptive fractional-order fuzzy control

Original Article

Normalized projection approach to group decision-making with hybrid decision information

Original Article

Density peaks clustering using geodesic distances

Original Article

Multi-label feature selection via feature manifold learning and sparsity regularization

Neuer Inhalt

    Bildnachweise