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 Processing Letters
Erschienen in: Neural Processing Letters 1/2018

12.10.2017

A New Virtual Samples-Based CRC Method for Face Recognition

verfasst von: Yali Peng, Lingjun Li, Shigang Liu, Tao Lei, Jie Wu

Erschienen in: Neural Processing Letters | Ausgabe 1/2018

Einloggen

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

search-config
loading …

Abstract

The research of automatic face recognition has attracted much attention from many researchers because of human faces’ uniqueness and usability. However, in the real-world applications, the acquisition equipment of face images is affected by illumination changes, facial expression variations, different postures and other environment factors, resulting in limited number of face images collected. This situation has become an obstacle to the development of face recognition technology. Therefore, in this paper, we utilize the information of the left-half face and right-half face to generate respectively two virtual ‘axis-symmetrical’ face images from an original face image and adopt collaborative representation based classification method (CRC) to perform classification. The first and second virtual face images convey more information of the right-half face and left-half face, respectively. Experiments have been performed on the Extended Yale_B, ORL, AR and FERET face databases and the experimental results show that our method can improve the recognition accuracy effectively.
Vorheriger Artikel Robust Low Rank Subspace Segmentation via Joint -Norm Minimization
Nächster Artikel Small-Scale Building Load Forecast based on Hybrid Forecast Engine

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
Literatur
1.
Zhao Q, Zhang D, Zhang L et al (2010) High resolution partial fingerprint alignment using pore-valley descriptors. Pattern Recogn 43(3):1050–1061CrossRefMATH
2.
Fei L, Zhang B, Xu Y et al (2016) Palmprint recognition using neighboring direction indicator. IEEE Trans Hum Mach Syst 46(6):787–798CrossRef
3.
4.
Xu Y, Fei L, Zhang D (2015) Combining left and right palmprint images for more accurate personal identification. IEEE Trans Image Process 24(2):549–559MathSciNetCrossRef
5.
Li Q, Xie B, You J et al (2016) Correlated logistic model with elastic net regularization for multilabel image classification. IEEE Trans Image Process 25(8):3801–3813MathSciNetCrossRef
6.
Xu Y, Fang X, Li X et al (2014) Data uncertainty in face recognition. IEEE Trans Cybern 44(10):1950–1961CrossRef
7.
Yang W, Sun C, Yang J et al (2011) Face recognition using kernel UDP. Neural Process Lett 34(2):177–192CrossRef
8.
Xu Y, Li Z, Yang J et al (2017) A survey of dictionary learning algorithms for face recognition. IEEE Access 5:8502–8514CrossRef
9.
Xu Y, Zhu Q, Fan Z et al (2013) Using the idea of the sparse representation to perform coarse-to-fine face recognition. Inf Sci 238(20):138–148MathSciNetCrossRef
10.
Yang W, Wang Z, Zhang B (2016) Face recognition using adaptive local ternary patterns method. Neurocomputing 213:183–190CrossRef
11.
Plamondon R, Srihari S (2000) On-line and off-line handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 22(1):63–84CrossRef
12.
Xu Y, Li Z, Zhang B et al (2017) Sample diversity, representation effectiveness and robust dictionary learning for face recognition. Inf Sci 375:171–182CrossRef
13.
Xu Y, Fang X, You J et al (2015) Noise-free representation based classification and face recognition experiments. Neurocomputing 147:307–314CrossRef
14.
Xu Y, Fang X, Zhu Q et al (2014) Modified minimum squared error algorithm for robust classification and face recognition experiments. Neurocomputing 135:253–2614CrossRef
15.
Lai Z, Xu Y, Jin Z (2014) Human gait recognition via sparse discriminant projection learning. IEEE Trans Circuits Syst Video Technol 24(10):1651–1662CrossRef
16.
Liu Z, Qiu Y, Peng Y et al (2017) Quaternion based maximum margin criterion method for color face recognition. Neural Process Lett 45(3):913–923CrossRef
17.
Yang W, Sun C, Du H et al (2011) Feature extraction using Laplacian maximum margin criterion. Neural Process Lett 33(1):99–110CrossRef
18.
Wang Z, Yang W, Shen F (2016) Face recognition using a low rank representation based projections method. Neural Process Lett 43(3):823–835CrossRef
19.
Wang G, Shi N, Shu Y et al (2016) Embedded manifold-based kernel fisher discriminant analysis for face recognition. Neural Process Lett 43(1):1–16CrossRef
20.
Xu Y, Lu Y (2015) Adaptive weighted fusion: a novel fusion approach for image classification. Neurocomputing 168:566–574CrossRef
21.
Liu W, Liu H, Tao D et al (2016) Multiview Hessian regularized logistic regression for action recognition. Signal Process 110:101–107CrossRef
22.
Yang W, Sun C, Zheng W (2016) A regularized least square based discriminative projections for feature extraction. Neurocomputing 175:198–205CrossRef
23.
Xu Y, Zhang D, Yang J et al (2011) A two-phase test sample sparse representation method for use with face recognition. IEEE Trans Circuits Syst Video Technol 21(9):1255–1262MathSciNetCrossRef
24.
Xu Y, Fang X, Wu J et al (2015) Discriminative transfer subspace learning via low-rank and sparse representation. IEEE Trans Image Process 25(2):850–863MathSciNetCrossRef
25.
Liu Z, Pu J, Xu M et al (2015) Face recognition via weighted two phase test sample sparse representation. Neural Process Lett 41(1):43–53CrossRef
26.
Liu T, Tao D (2016) On the performance of Manhattan nonnegative matrix factorization. IEEE Trans Neural Netw Learn Syst 27(9):1851–1863MathSciNetCrossRef
27.
Jin T, Liu Z, Yu Z et al (2017) Locality preserving collaborative representation for face recognition. Neural Process Lett 45(3):967–979CrossRef
28.
29.
Du B, Zhang L, Tao D et al (2013) Unsupervised transfer learning for target detection from hyperspectral images. Neurocomputing 120:72–82CrossRef
30.
Liu T, Tao D, Song M et al (2017) Algorithm-dependent generalization bounds for multi-task learning. IEEE Trans Pattern Anal Mach Intell 39(2):227–241CrossRef
31.
Du B, Zhang L (2014) A discriminative metric learning based anomaly detection method. IEEE Trans Geosci Remote Sens 52(11):6844–6857CrossRef
32.
Ding C, Tao D (2016) A comprehensive survey on pose-invariant face recognition. ACM Trans Intell Syst Technol 7(3):1–42CrossRef
33.
Liu T, Lugosi G, Neu G et al (2017) Algorithmic stability and hypothesis complexity. In: Proceedings of the 34th international conference on machine learning, pp 2159–2167
34.
Gong C, Liu T, Tao D et al (2015) Deformed graph Laplacian for semisupervised learning. IEEE Trans Neural Netw Learn Syst 26(10):2261–2274MathSciNetCrossRef
35.
Zhang X, Peng Y, Liu S et al (2017) A supervised dimensionality reduction method based sparse representation for face recognition. J Mod Opt 64(8):799–806CrossRef
36.
Liu T, Tao D (2016) Classification with noisy labels by importance reweighting. IEEE Trans Pattern Anal Mach Intell 38(3):447–461MathSciNetCrossRef
37.
Liu S, Li L, Peng Y et al (2017) An improved sparse representation method for image classification. IET Comput Vision 11(4):319–330CrossRef
38.
Gong C, Tao D, Liu W et al (2017) Label propagation via teaching-to-learn and learning-to-teach. IEEE Trans Neural Netw Learn Syst 28(6):1452–1465CrossRef
39.
Du B, Zhang M, Zhang L et al (2017) PLTD: patch-based low-rank tensor decomposition for hyperspectral images. IEEE Trans Multimed 19(1):67–79CrossRef
40.
Liu S, Peng Y, Ben X et al (2016) A novel label learning algorithm for face recognition. Signal Process 124:141–146CrossRef
41.
Gong C, Fu K, Wu Q et al (2014) Semi-supervised classification with pairwise constraints. Neurocomputing 139:130–137CrossRef
42.
Du B, Xiong W, Wu J et al (2016) Stacked convolutional denoising auto-encoders for feature representation. IEEE Trans Cybern 47(4):1017–1027CrossRef
43.
Jung H, Hwang B, Lee S (2004) Authenticating corrupted face image based on noise model. In: Proceedings of the IEEE international conference on automatic face and gesture recognition, pp 272–277
44.
Thian N, Marcel S, Bengio S (2003) Improving face authentication using virtual samples, In: IEEE international conference on acoustics, vol III, pp 233–236
45.
Tang B, Luo S, Huang H (2004) High performance face recognition system by creating virtual sample. Proc Int Conf Neural Netw Signal Process 2:972–975
46.
Wu J, Zhou Z (2002) Face recognition with one training image per person. Pattern Recogn Lett 23(14):1711–1719CrossRefMATH
47.
Ryu Y, Sy O (2002) Simple hybrid classifier for face recognition with adaptively generated virtual data. Pattern Recogn Lett 23(7):833–841CrossRefMATH
48.
Xu Y, Zhu X, Li Z et al (2013) Using the original and ‘symmetrical face’ training samples to perform representation based two-step face recognition. Pattern Recogn 46(4):1151–1158CrossRef
49.
Yang W, Wang Z, Sun C (2015) A collaborative representation based projections method for feature extraction. Pattern Recogn 48(1):20–27CrossRef
50.
Du D, Zhang Y, Zhang L et al (2016) Beyond the sparsity-based target detector: a hybrid sparsity and statistics based detector for hyperspectral images. IEEE Trans Image Process 25(11):5345–5357MathSciNetCrossRef
51.
Liu W, Ma T, Xie Q et al (2017) LMAE: a large margin auto-encoders for classification. Signal Process 141:137–143CrossRef
52.
Xu C, TLiu T, Tao D et al (2016) Local rademacher complexity for multi-label learning. IEEE Trans Image Process 25(3):1495–1507MathSciNetCrossRef
53.
54.
Li J, Xu C, Yang W et al (2017) Discriminative multi-view interactive image re-ranking. IEEE Trans Image Process 26(7):3113–3127MathSciNetCrossRef
55.
Shen J, Zuo X, Liu H et al (2017) Supervised local high-order differential channel feature learning for pedestrian detection. Neural Process Lett 45(3):1025–1037CrossRef
56.
Xu Y, Li X, Yang J et al (2014) Integrating conventional and inverse representation for face recognition. IEEE Trans Cybern 44(10):1738–1746CrossRef
57.
Liu Z, Pu J, Wu Q et al (2015) Using the original and symmetrical face training samples to perform collaborative representation for face recognition. Opt Int J Light Electron Opt 127(4):1900–1904CrossRef
58.
Xu Y, Zhang Z, Lu G et al (2016) Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification. Pattern Recogn 54:68–82CrossRef
59.
Lai Z, Wong W, Xu Y et al (2016) Approximate orthogonal sparse embedding for dimensionality reduction. IEEE Trans Neural Netw Learn Syst 27(4):723–735MathSciNetCrossRef
60.
61.
62.
Ding C, Choi J, Tao D (2016) Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE Trans Pattern Anal Mach Intell 38(3):518–531CrossRef
63.
Liu S, Zhang X, Peng Y et al (2016) Virtual images inspired consolidate collaborative representation based classification method for face recognition. J Mod Opt 63(12):1181–1188CrossRef
64.
Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition? In: International conference on computer vision, pp 471–478
65.
Wright J, Yang A, Ganesh A et al (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–27CrossRef
66.
Chi Y, Porikli F (2014) Classification and boosting with multiple collaborative representations. IEEE Trans Pattern Anal Mach Intell 36(8):1519–1531CrossRef
67.
Timofte R, Van Gool L (2014) Adaptive and weighted collaborative representations for image classification. Pattern Recogn Lett 43:127–135CrossRef
68.
Wu J, Timofte R, Gool L (2015) Learned collaborative representations for image classification. In: IEEE winter conference on applications of computer vision. IEEE Computer Society, pp 456–463
69.
Cai S, Zhang L, Zuo W et al (2016) A probabilistic collaborative representation based approach for pattern classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2950–2959
70.
Imran N, Roberto T, Mohammed B (2010) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell 32(11):2106–2112CrossRef
71.
Yang A, Ganesh A, Sastry S et al (2010) Fast l1-Minimization algorithms and an application in robust race recognition: a review. In: IEEE international conference on mage processing, pp 1849–1852
72.
Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183–202MathSciNetCrossRefMATH
73.
Gross R, Mathews I, Cohn J et al (2008) MultiPIE. In: IEEE international conference on automatic face and gesture recognition
74.
Xu Y, Zhu Q, Fan Z et al (2013) Coarse to fine K nearest neighbor classifier. Pattern Recogn Lett 34(9):980–986CrossRef
75.
Xu Y, Fan Z, Zhu Q (2012) Feature space-based human face image representation and recognition. Opt Eng 51(1):017205-017205-8CrossRef
76.
Xu Y, Zhu Q, Chen Y et al (2013) An improvement to the nearest neighbor classifier and face recognition experiments. Int J Innov Comput Inf Control 9(2):543–554
77.
78.
79.
80.
Metadaten
Titel
A New Virtual Samples-Based CRC Method for Face Recognition
verfasst von
Yali Peng
Lingjun Li
Shigang Liu
Tao Lei
Jie Wu
Publikationsdatum
12.10.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9721-4

Weitere Artikel der Ausgabe 1/2018

Neural Processing Letters 1/2018 Zur Ausgabe

OriginalPaper

Gradient Neural Network with Nonlinear Activation for Computing Inner Inverses and the Drazin Inverse

OriginalPaper

Small-Scale Building Load Forecast based on Hybrid Forecast Engine

OriginalPaper

Finite-Time Stability for Delayed Complex-Valued BAM Neural Networks

OriginalPaper

Pruning the Ensemble of ANN Based on Decision Tree Induction

OriginalPaper

Fitness Landscape Analysis of Weight-Elimination Neural Networks

BriefCommunication

Imposing Class-Wise Feature Similarity in Stacked Autoencoders by Nuclear Norm Regularization

Neuer Inhalt

    Bildnachweise