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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 6/2018

30-12-2016 | Original Article

Statistical binary patterns and post-competitive representation for pattern recognition

Authors: Mohamed Anouar Borgi, Thanh Phuong Nguyen, Demetrio Labate, Chokri Ben Amar

Published in: International Journal of Machine Learning and Cybernetics | Issue 6/2018

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Abstract

During the last decade, sparse representations have been successfully applied to design high-performing classification algorithms such as the classical sparse representation based classification (SRC) algorithm. More recently, collaborative representation based classification (CRC) has emerged as a very powerful approach, especially for face recognition. CRC takes advantage of SRC through the notion of collaborative representation, relying on the observation that the collaborative property is more crucial for classification than the l 1-norm sparsity constraint on coding coefficients used in SRC. This paper follows the same general philosophy of CRC and its main novelty is the application of a virtual collaborative projection (VCP) routine designed to train images of every class against the other classes to improve fidelity before the projection of the query image. We combine this routine with a method of local feature extraction based on high-order statistical moments to further improve the representation. We demonstrate using extensive experiments of face recognition and classification that our approach performs very competitively with respect to state-of-the-art classification methods. For instance, using the AR face dataset, our method reaches 100% of accuracy for dimensionality 300.
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Literature
1.
Borgi MA, Labate D, El’arbi M, Amar CB (2015) Sparse multi-stage regularized feature learning for robust face recognition. Expert Syst Appl 42(1):269–279CrossRef
2.
Wright J, Yang AY, Ganesh A et al (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227CrossRef
3.
Theodorakopoulos I, Rigas I, Economou G, Fotopoulos S (2011) Face recognition via local sparse coding. In: Proceedings of the ICCV, pp 1647–1652
4.
Gao S, Tsang I, Chia L (2010) Kernel sparse representation for image classification and face recognition. In: Proceedings of the ECCV, pp 1–14
5.
Yang M, Zhang L (2010) Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary. In: Proceedings of the ECCV, pp 448–461
6.
Yang M, Zhang L, Yang J, Zhang D (2011) Robust sparse coding for face recognition. In: Proceedings of the ICCV, pp 625–632
7.
8.
He R, Zheng WS, Hu BG (2011) Maximum correntropy criterion for robust face recognition. IEEE Trans Pattern Anal Mach Intell 33(8):1561–1576CrossRef
9.
Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition? In: Proceedings of the ICCV, pp 471–478
10.
Yang M, Zhang L, Zhang D, Wang S (2012) Relaxed collaborative representation for pattern classification. In: Proceedings of the ICCV, pp 2224–2231
11.
Xu Y, Zhang D, Yang J, Yang J-Y (2011) A two-phase test sample sparse representation method for use with face recognition. IEEE Trans Circuits Syst Video Technol 21(9):1255–1262MathSciNetCrossRef
12.
13.
Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation. In: Proceedings of the ICCV, pp 543–550
14.
Feng Z, Yang M, Zhang L, Liu Y, Zhang D (2013) Joint discriminative dimensionality reduction and dictionary learning for face recognition. Pattern Recogn 46(8):2134–2143CrossRef
15.
Gu S, Zhang L, Zuo W et al (2014) Projective dictionary pair learning for pattern classification. In: Proceeding of advances in neural information processing systems, pp 793–801
16.
Cai S, Zuo W, Zhang L (2014) Support vector guided dictionary learning. In: Proceedings of the European conference on computer vision, pp 624–639
17.
18.
Cai S, Zhang L, Zuo W, Feng X (2016) A probabilistic collaborative representation based approach for pattern classification. In CVPR, pp 2950–2959
19.
Lades M, Vorbrüggen JC, Buhmann J et al (1993) Distortion invariant object recognition in the dynamic link architecture. IEEE Trans Comput 42(3):300–311CrossRef
20.
Liu C, 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
21.
22.
Timo A, Abdenour H, Matti P (2004) Face recognition with local binary patterns. In: Proceedings of the ECCV, pp 469–481
23.
Ojala T, Pietikäinen M, Mäenpää T (2002) Multi-resolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefMATH
24.
Zhang W, Shan S, 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 ICCV, pp 786–791
25.
Zhang W, Shan S, Chen X, Gao W (2009) Are Gabor phases really useless for face recognition?. Pattern Anal Appl 12(3):301–307MathSciNetCrossRef
26.
Zhang B, Shan S, Chen X, Gao W (2007) Histogram of Gabor phase patterns (HGPP): a novel object representation approach for face recognition. IEEE Trans Image Process 16(1):57–68MathSciNetCrossRef
27.
Xie SF, Shan SG, Chen XL, Chen J (2010) Fusing local patterns of Gabor magnitude and phase for face recognition. IEEE Trans Image Process 19(5):1349–1361MathSciNetCrossRefMATH
28.
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
29.
Turk M, Pentland A (1991) Eigenfaces for recognition. J. Cognitive. Neuroscience 3(1):71–86
30.
Belhumeur PN, Hespanha JP, Kriengman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRef
31.
Borgi MA, El’arbi M, Labate D, Amar CB (2015) Regularized directional feature learning for face recognition. Multimedia Tools Appl 74(24):11281–11295CrossRef
32.
Borgi MA, Labate D, El’Arbi M, Amar CB (2014) Regularized Shearlet network for face recognition using single sample per person. In: Proceedings of the ICASSP, pp 514–518
33.
Borgi MA, Labate D, El'arbi M, Amar CB (2014) ShearFace: efficient extraction of anisotropic features for face recognition. In: Proceedings of the ICPR, pp 1806–1811
34.
Borgi MA, Labate D, El'arbi M, Amar CB (2014) Sparse multi-regularized shearlet-network using convex relaxation for face recognition. In: Proceedings of the ICPR, pp 4636–4641
35.
Borgi MA, Labate D, El'arbi M, Amar CB (2013) Shearlet network-based sparse coding augmented by facial texture features for face recognition. In: Proceedings of the ICIAP, pp 611–620
36.
Yang M, Zhang L, Shiu SC, Zhang D (2013) Robust kernel representation with statistical local features for face recognition. IEEE Trans Neural Netw Learn Syst 24(6):900–912CrossRef
37.
Nguyen TP, Vu NS, Manzanera A (2016) Statistical binary patterns for rotational invariant texture classification. Neurocomputing 173:1565–1577CrossRef
38.
Guo ZH, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663MathSciNetCrossRefMATH
39.
Ojala T, Maenpaa T, Pietikainen M et al (2002) Outex—new framework for empirical evaluation of texture analysis algorithms. In: Proceedings of the ICPR, pp 701–706
40.
Martinez A, Benavente R (1998) The AR face database. CVC technical report 24
41.
Georghiades A, Belhumeur P, Kriegman D (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE PAMI 23(6):643–660CrossRef
42.
Lee K, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE PAMI 27(5):684–698CrossRef
43.
Li SZ, Lu J (1999) Face recognition using nearest feature line method. IEEE Trans Neural Netw 10(2):439–443CrossRef
44.
Naseem I, Togneri R, Bennamoun M (2010) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell 32(11):2106–2112CrossRef
45.
Wang JJ, Yang JC et al (2010) Locality-constrained linear coding for image classification. In: Proceedings of the CVPR, pp 3360–3371
46.
Gross R, Matthews I et al (2010) Multi-PIE. Image Vis Comput 28(5):807–813CrossRef
47.
48.
Phillips PJ, Flynn PJ et al (2005) Overview of the face recognition grand challenge. In: Proceedings of the CVPR, pp 947–954
49.
Zhang L, Yang M et al (2011) Collaborative representation based classification for face recognition. Technical report. arXiv: 1204.2358
50.
Borgi MA, El’arbi M, Labate D, Amar CB (2014) Face, gender and race classification using multi-regularized features learning. In: Proceedings of the ICIP, pp 5277–5281
51.
Thomaz E, Giraldi GA (2010) A new ranking method for principal components analysis and its application to face image analysis. Image Vis Comput 28(6):902–913CrossRef
52.
Yang M, Zhang L, Feng X, Zhang D (2014) Sparse representation based fisher discrimination dictionary learning for image classification. Int J Comput Vis 109(3):209–232MathSciNetCrossRefMATH
53.
Nilsback M, Zisserman A (2006) A visual vocabulary for flower classification. In: Proceedings of the CVPR, pp 1447–1454
54.
Yuan XT, Yan SC (2010) Visual classification with multitask joint sparse representation. In: Proceedings of the CVPR, pp 3493–3500
55.
Nilsback M, Zisserman A (2008) Automated flower classification over a large number of classes. In: Proceedings of the ICCVGIP, pp 722–729
56.
Gehler P, Nowozin S (2009) On feature combination for multiclass object classification. In: Proceedings of the ICCV, pp 221–228
57.
Rodriguez MD, Ahmed J, Shah M (2008) Action MACH a spatio-temporal maximum average correlation height filter for action recognition. In: Proceedings of the CVPR
58.
Sadanand S, Corso JJ (2012) Action bank: a high-level representation of activity in video. In: Proceedings of the CVPR, pp 1234–1241
59.
Yao A, Gall J, Van Gool LJ (2010) A hough transform-based voting framework for action recognition. In: Proceedings of the CVPR, pp 2061–2068
60.
Yeffet L, Wolf L (2009) Local trinary patterns for human action recognition. In: Proceedings of the ICCV, pp 492–497
61.
Wang H, Ullah MM et al (2009) Evaluation of local spatio-temporal features for action recognition. In: Proceedings of the BMVC, pp 1–11
Metadata
Title
Statistical binary patterns and post-competitive representation for pattern recognition
Authors
Mohamed Anouar Borgi
Thanh Phuong Nguyen
Demetrio Labate
Chokri Ben Amar
Publication date
30-12-2016
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 6/2018
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0625-9

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