nach oben

Erschienen in:

2017 | OriginalPaper | Buchkapitel

Eigen-Aging Reference Coding for Cross-Age Face Verification and Retrieval

verfasst von : Kaihua Tang, Sei-ichiro Kamata, Xiaonan Hou, Shouhong Ding, Lizhuang Ma

Erschienen in: Computer Vision – ACCV 2016

Verlag: Springer International Publishing

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Recent works have achieved near or over human performance in traditional face recognition under PIE (pose, illumination and expression) variation. However, few works focus on the cross-age face recognition task, which means identifying the faces from same person at different ages. Taking human-aging into consideration broadens the application area of face recognition. It comes at the cost of making existing algorithms hard to maintain effectiveness. This paper presents a new reference based approach to address cross-age problem, called Eigen-Aging Reference Coding (EARC). Different from other existing reference based methods, our reference traces eigen faces instead of specific individuals. The proposed reference has smaller size and contains more useful information. To the best of our knowledge, we achieve state-of-the-art performance and speed on CACD dataset, the largest public face dataset containing significant aging information.

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 Facial Expression-Aware Face Frontalization

Nächstes Kapitel Consistent Sparse Representation for Video-Based Face Recognition

Chen, B.-C., Chen, C.-S., Hsu, W.H.: Cross-age reference coding for age-invariant face recognition and retrieval. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 768–783. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10599-4_49

Chen, D., Cao, X., Wen, F., Sun, J.: Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3025–3032 (2013)

Barkan, O., Weill, J., Wolf, L., Aronowitz, H.: Fast high dimensional vector multiplication face recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1960–1967 (2013)

Zhu, K., Gong, D., Li, Z., Tang, X.: Orthogonal Gaussian process for automatic age estimation. In: Proceedings of the ACM International Conference on Multimedia, pp. 857–860. ACM (2014)

Lanitis, A., Taylor, C.J., Cootes, T.F.: Toward automatic simulation of aging effects on face images. IEEE Trans. Pattern Anal. Mach. Intell. 24, 442–455 (2002)CrossRef

Du, J.X., Zhai, C.M., Ye, Y.Q.: Face aging simulation and recognition based on NMF algorithm with sparseness constraints. Neurocomputing 116, 250–259 (2013)CrossRef

Park, U., Tong, Y., Jain, A.K.: Age-invariant face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32, 947–954 (2010)CrossRef

Li, Z., Gong, D., Li, X., Tao, D.: Aging face recognition: a hierarchical learning model based on local patterns selection. IEEE Trans. Image Process. 25, 2146–2154 (2016)MathSciNetCrossRef

Gong, D., Li, Z., Lin, D., Liu, J., Tang, X.: Hidden factor analysis for age invariant face recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2872–2879 (2013)

10.

Yin, Q., Tang, X., Sun, J.: An associate-predict model for face recognition. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 497–504. IEEE (2011)

11.

Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Attribute and simile classifiers for face verification. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 365–372. IEEE (2009)

12.

Li, Z., Gong, D., Li, X., Tao, D.: Learning compact feature descriptor and adaptive matching framework for face recognition. IEEE Trans. Image Process. 24, 2736–2745 (2015)MathSciNetCrossRef

13.

Li, Y., Meng, L., Feng, J., Wu, J.: Downsampling sparse representation and discriminant information aided occluded face recognition. Sci. Chin. Inf. Sci. 57, 1–8 (2014)

14.

Fu, Y., Huang, T.S.: Human age estimation with regression on discriminative aging manifold. IEEE Trans. Multimedia 10, 578–584 (2008)CrossRef

15.

Geng, X., Zhou, Z.H., Smith-Miles, K.: Automatic age estimation based on facial aging patterns. IEEE Trans. Pattern Anal. Mach. Intell. 29, 2234–2240 (2007)CrossRef

16.

Suo, J., Chen, X., Shan, S., Gao, W.: Learning long term face aging patterns from partially dense aging databases. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 622–629. IEEE (2009)

17.

Tsumura, N., Ojima, N., Sato, K., Shiraishi, M., Shimizu, H., Nabeshima, H., Akazaki, S., Hori, K., Miyake, Y.: Image-based skin color and texture analysis/synthesis by extracting hemoglobin and melanin information in the skin. ACM Trans. Graph. (TOG) 22, 770–779 (2003)CrossRef

18.

Ling, H., Soatto, S., Ramanathan, N., Jacobs, D.W.: Face verification across age progression using discriminative methods. IEEE Trans. Inf. Forensics Secur. 5, 82–91 (2010)CrossRef

19.

Klare, B., Jain, A.K.: Face recognition across time lapse: on learning feature subspaces. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–8. IEEE (2011)

20.

Ricanek Jr., K., Tesafaye, T.: MORPH: a longitudinal image database of normal adult age-progression. In: 2006 7th International Conference on Automatic Face and Gesture Recognition, FGR 2006, pp, 341–345. IEEE (2006)

21.

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)

22.

Heseltine, T., Pears, N., Austin, J.: Evaluation of image preprocessing techniques for eigenface-based face recognition. In: Second International Conference on Image and Graphics, pp. 677–685. International Society for Optics and Photonics (2002)

Titel: Eigen-Aging Reference Coding for Cross-Age Face Verification and Retrieval
verfasst von: Kaihua Tang
Sei-ichiro Kamata
Xiaonan Hou
Shouhong Ding
Lizhuang Ma
Verlag: Springer International Publishing
Buch: Computer Vision – ACCV 2016
Print ISBN: 978-3-319-54186-0

Electronic ISBN: 978-3-319-54187-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-54187-7_26

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"

Premium Partner