Abstract
This paper deals with the influence of age progression on the performance of face verification systems. This is a challenging and largely open research problem that deserves more and more attention. Aging affects both the shape of the face and its texture, leading to a failure in the face verification task. In this paper, the aging influence on the face verification system using local ternary patterns is managed by a Q-stack aging model, which uses the age as a class-independent metadata quality measure together with baseline classifier scores in order to obtain better recognition rates. This allows for increased long-term class separation by a decision boundary in the score-quality measure space using a short-term enrolment model. This new method, based on the concept of classifier stacking, compares favorably with the conventional face verification approach which uses decision boundary calculated only in the score space at the time of enrolment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ramanathan, N., Chellappa, R.: Face Verification Across Age Progression. IEEE Trans. Image Processing. 15, 3349–3361 (2006)
Patterson, E., Sethuram, A., Albert, M., Ricanek, K., King, M.: Aspects of Age Variation in Facial Morphology Affecting Biometrics. In: IEEE Conference on Biometrics: Theory, Applications, and Systems (BTAS 2007), Washington, D.C., USA, September 27-29 (2007)
Scandrett, C., Solomon, J., Gibson, S.J.: A person-specific, rigorous aging model of the human face. Pattern Recognition Letters 27, 1776–1787 (2006)
Flynn, P.: Biometrics databases. In: Jain, A., et al. (eds.) Handbook of Biometrics, ch. 25, pp. 529–548. Springer, New York (2008)
Ricanek, K., Tesafaye, T.: MORPH: A longitudinal image database of normal adult age-progression. In: 7th International Conference on Automatic Face and Gesture Recognition (FGR 2006), April 2006, pp. 341–345 (2006)
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 168–182. Springer, Heidelberg (2007)
Kryszczuk, K.M., Drygajlo, A.: Improving classification with class-independent quality measures: Q-stack in face verification. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 1124–1133. Springer, Heidelberg (2007)
Hicklin, A., Khanna, R.: The Role of Data Quality in Biometric Systems. White Paper. Mitretek Systems (February 2006)
Kryszczuk, K., Drygajlo, A.: Q-stack: uni- and multimodal classifier stacking with quality measures. In: International Workshop on Multiple Classifier Systems, Prague, Czech Republic (May 2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Drygajlo, A., Li, W., Zhu, K. (2009). Verification of Aging Faces Using Local Ternary Patterns and Q-Stack Classifier. In: Fierrez, J., Ortega-Garcia, J., Esposito, A., Drygajlo, A., Faundez-Zanuy, M. (eds) Biometric ID Management and Multimodal Communication. BioID 2009. Lecture Notes in Computer Science, vol 5707. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04391-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-04391-8_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04390-1
Online ISBN: 978-3-642-04391-8
eBook Packages: Computer ScienceComputer Science (R0)