Abstract
This paper proposes a novel technique that uses Active Appearance Models (AAMs) and Ensemble of classifiers for age estimation. In this technique, features are extracted from face images by AAMs and a global classifier is then used to obtain an idea about the age by distinguishing between child/teen-hood and adulthood, before age estimation. This is done by an ensemble containing various classifiers trained on multiple dissimilarities and thereby which reduces misclassification error. Different aging functions are considered for the classified images to estimate age more accurately. Experiments are performed on the publicly available FG-NET database. The method is found to be a good age estimator.
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
Lanitis, A., Taylor, C.J., Cootes, T.F.: Toward automatic simulation of aging effects on face images. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 442–455 (2002)
Chellappa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of faces: a survey. Proceedings of the IEEE 83(5), 705–741 (1995)
Fasel, B., Luettin, J.: Automatic facial expression analysis: a survey. Pattern Recognition 36(1), 259–275 (2003)
O’toole, A.J., Deffenbacher, K.A., Valentin, D., Mckee, K., Huff, D., Abdi, H.: The Perception of Face Gender: The Role of Stimulus Structure in Recognition and Classification. Memory and Cognition 26, 146–160 (1997)
Albert, M., Ricanek, K., Patterson, E.: A review of the literature on the aging adult skull and face: Implications for forensic science research and applications. Forensic Science International 172(1), 1–9 (2007)
Magnus, C., Forsberg: Facial morphology and ageing: a longitudinal cephalometric investigation of young adults. European Journal of Orthodontics 1(1), 15–23 (1979)
Luu, K., Ricanek, K., Bui, T.D., Suen, C.Y.: Age estimation using active appearance models and support vector machine regression. In: Proceedings of the 3rd IEEE International Conference on Biometrics: Theory, Applications and systems, pp. 314–318 (2010)
Edwards, G.J., Lanitis, A., Taylor, C.J., Cootes, T.F.: Statistical models of face images – improving specificity. Image and Vision Computing 16(3), 203–211 (1998)
Ramanathan, N., Chellappa, R.: Face Verification across Age Progression. In: Proceeding of IEEE Conference of Computer Vision and Pattern Recognition, pp. 462–469 (2005)
Fu, Y., Huang, T.S.: Human Age Estimation With Regression on Discriminative Aging Manifold. IEEE Transactions on Multimedia 10(4), 578–584 (2008)
Kwon, Y.H., da Vitoria Lobo, N.: Age Classification from Facial Images. Computer Vision and Image Understanding 74(1), 1–21 (1999)
Geng, X., Zhou, Z.-H., Zhang, Y., Li, G., Dai, H.: Learning from facial aging patterns for automatic age estimation. ACM Multimedia, 307–316 (2006)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 681–685 (2001)
Kang, H., Cootes, T.F., Taylor, C.J.: A Comparison of Face Verification Algorithms using Appearance Models. In: British Machine Vision Conference, vol. 2, pp. 477–486 (2002)
Tang, F., Deng, B.: Facial Expression Recognition using AAM and Local Facial Features. In: Proceedings of the Third International Conference on Natural Computation ICNC 2007, vol. 02, pp. 632–635 (2007)
Kuncheva, L.I., Whitaker, C.J.: Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy. Machine Learning 51(2), 181–207 (2003)
Pekalska, E., Paclík, P., Duin, R.P.W.: A Generalized Kernel Approach to Dissimilarity-based Classification. Journal of Machine Learning Research 2, 175–211 (2001)
Blanco, Á., Martín-Merino, M., De Las Rivas, J.: Ensemble of Support Vector Machines to Improve the Cancer Class Prediction Based on the Gene Expression Profiles. Innovations in Hybrid Intelligent Systems 44, 393–400 (2008)
Chen, Chun-houh, Wolfgang, Unwin, A., Cox, M.A.A., Cox, T.F.: Multidimensional Scaling Handbook of Data Visualization, pp. 315–347 (2008)
The FG-NET Aging Database, http://www.fgnet.rsunit.com/
Smola, A.J., Scholkopf, B.: A tutorial on support vector regression Statistics and Computing, vol. 14(3), pp. 199–222 (2004)
Wold, S., Eriksson, L., Kettaneh, N.: PLS in Data Mining and Data Integration. Handbook of Partial Least Squares, pp. 327–357 (2010)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kohli, S., Prakash, S., Gupta, P. (2011). Age Estimation Using Active Appearance Models and Ensemble of Classifiers with Dissimilarity-Based Classification. In: Huang, DS., Gan, Y., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing. ICIC 2011. Lecture Notes in Computer Science, vol 6838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24728-6_44
Download citation
DOI: https://doi.org/10.1007/978-3-642-24728-6_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24727-9
Online ISBN: 978-3-642-24728-6
eBook Packages: Computer ScienceComputer Science (R0)