nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Single Sample Face Recognition by Sparse Recovery of Deep-Learned LDA Features

verfasst von : Matteo Bodini, Alessandro D’Amelio, Giuliano Grossi, Raffaella Lanzarotti, Jianyi Lin

Erschienen in: Advanced Concepts for Intelligent Vision Systems

Verlag: Springer International Publishing

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Single Sample Per Person (SSPP) Face Recognition is receiving a significant attention due to the challenges it opens especially when conceived for real applications under unconstrained environments. In this paper we propose a solution combining the effectiveness of deep convolutional neural networks (DCNN) feature characterization, the discriminative capability of linear discriminant analysis (LDA), and the efficacy of a sparsity based classifier built on the \(k\)-LiMapS algorithm. Experiments on the public LFW dataset prove the method robustness to solve the SSPP problem, outperforming several state-of-the-art methods.

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

Vorheriges Kapitel Enhanced Line Local Binary Patterns (EL-LBP): An Efficient Image Representation for Face Recognition

Nächstes Kapitel Recursive Chaining of Reversible Image-to-Image Translators for Face Aging

A demo code is available on the website: https://github.com/phuselab/SSPP-face_recognition.

Strictly speaking the \(\ell _0\)-norm is not actually a norm, it is a cardinality function counting the number of nonzero elements in a vector.

For a given of vector \(\alpha \), the support \({{\mathrm{ supp}}}(\alpha )\) is the index pool of nonzero entries of \(\alpha \).

Here we have simplified the notation to refer to the sparse solutions \(\alpha _{l,f}\) to \(\alpha _j\), knowing that the couple set (l, f) has cardinality d.

Adamo, A., Grossi, G., Lanzarotti, R.: Local features and sparse representation for face recognition with partial occlusions. In: 2013 IEEE International Conference on Image Processing, pp. 3008–3012 (2013)

Adamo, A., Grossi, G., Lanzarotti, R., Lin, J.: Sparse decomposition by iterating lipschitzian-type mappings. Theor. Comput. Sci. 664, 12–28 (2017)MathSciNetCrossRefMATH

Cuculo, V., Lanzarotti, R., Boccignone, G.: Using sparse coding for landmark localization in facial expressions. In: 5th European Workshop on Visual Information Processing (EUVIP), pp. 1–6, December 2014

Deng, W., Hu, J., Zhou, X., Guo, J.: Equidistant prototypes embedding for single sample based face recognition with generic learning and incremental learning. Pattern Recogn. 47(12), 3738–3749 (2014)CrossRef

Gao, Y., Ma, J., Yuille, A.L.: Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples. IEEE Trans. Image Process. 26(5), 2545–2560 (2017)MathSciNetCrossRefMATH

Grossi, G., Lanzarotti, R., Lin, J.: Robust face recognition providing the identity and its reliability degree combining sparse representation and multiple features. Int. J. Pattern Recognit. Artif. Intell. 30(10) (2016)

Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07–49, University of Massachusetts, Amherst (2007)

Lahasan, B., Lutfi, S.L., San-Segundo, R.: A survey on techniques to handle face recognition challenges: occlusion, single sample per subject and expression. Artif. Intell. Rev., 1–31 (2017)

Liu, F., Tang, J., Song, Y., Bi, Y., Yang, S.: Local structure based multi-phase collaborative representation for face recognition with single sample per person. Inf. Sci. (Ny) 346–347, 198–215 (2016)

10.

Natarajan, B.K.: Sparse approximate solutions to linear systems. SIAM J. Comput. 24, 227–234 (1995)MathSciNetCrossRefMATH

11.

Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: British Machine Vision, pp. 1–12 (2015)

12.

Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. CoRR abs/1712.04621 (2017)

13.

Rao, C.R.: The utilization of multiple measurements in problems of biological classification. J. R. Stat. Soc.-Ser. B 10(2), 159–203 (1948)MathSciNetMATH

14.

Shang, K., Huang, Z.H., Liu, W., Li, Z.M.: A single gallery-based face recognition using extended joint sparse representation. Appl. Math. Comput. 320, 99–115 (2018)MATH

15.

Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898. IEEE (2014)

16.

Wiskott, L., Fellous, J., Kruger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. In: Intelligent Biometric Techniques in Fingerprints and Face Recognition, pp. 355–396 (1999)

17.

Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–27 (2008)CrossRef

18.

Yang, M., Wang, X., Zeng, G., Shen, L.: Joint and collaborative representation with local adaptive convolution feature for face recognition with single sample per person. Pattern Recogn. 66, 117–128 (2017)CrossRef

19.

Zhang, Z., Xu, Y., Yang, J., Li, X., Zhang, D.: A survey of sparse representation: algorithms and applications. IEEE Access 3, 490–530 (2015)CrossRef

20.

Zhuo, T.: Face recognition from a single image per person using deep architecture neural networks. Clust. Comput. 19(1), 73–77 (2016)CrossRef

Titel: Single Sample Face Recognition by Sparse Recovery of Deep-Learned LDA Features
verfasst von: Matteo Bodini
Alessandro D’Amelio
Giuliano Grossi
Raffaella Lanzarotti
Jianyi Lin
Verlag: Springer International Publishing
Buch: Advanced Concepts for Intelligent Vision Systems
Print ISBN: 978-3-030-01448-3

Electronic ISBN: 978-3-030-01449-0

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01449-0_25

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"