Abstract
A method for estimating age and gender using multiple local patches is proposed in this thesis. We use the histogram of rotation-invariant local binary pattern as our features to train the SVM model. We further introduce the shifting and scaling of the local patches to enhance the accuracy of the estimation. Our proposed method not only provides accurate results but also can be incorporated with other methods to further improve their accuracy.
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
Boser, B.E., Guyon, I., Vapnik, V.: A Training Algorithm for Optimal Margin Classifiers. In: Proceedings of the Annual Workshop on Computational Learning Theory, Pittsburgh, PA, pp. 144–152 (1992)
Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology 2, 1–27 (2011)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)
Cortes, C., Vapnik, V.: Support-Vector Network. Machine Learning 20(3), 273–297 (1995)
Crowley, J.L.: The FG-NET Aging Database (2010), http://www-prima.inrialpes.fr/FGnet/
Fu, Y., Guo, G., Huang, T.S.: Age Synthesis and Estimation via Faces: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(11), 1955–1976 (2010)
Fu, Y., Xu, Y., Huang, T.S.: Estimating Human Ages by Manifold Analysis of Face Pictures and Regression on Aging Features. In: Proceedings of IEEE Conference Multimedia and Expo, Beijing, China, pp. 1383–1386 (2007)
Fukai, H., Takimoto, H., Mitsukura, Y., Fukumi, M.: Apparent Age Estimation System Based on Age Perception. In: Proceedings of SICE Annual Conference, Takamatsu, Japan, pp. 2808–2812 (2007)
Guo, G., Mu, G., Fu, Y., Huang, T.S.: Human Age Estimation Using Bio Inspired Features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, pp. 112–119 (2009)
Guo, G., Fu, Y., Dyer, C., Huang, T.S.: Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression. IEEE Transactions on Image Processing 17(7), 1178–1188 (2008)
Guo, G., Fu, Y., Huang, T.S., Dyer, C.: Locally Adjusted Robust Regression for Human Age Estimation. In: Proceedings of IEEE Workshop on Applications of Computer Vision, Copper Mountain, CO, pp. 1–6 (2008)
Geng, X., Zhou, Z.-H., Zhang, Y., Li, G., Dai, H.: Learning from Facial Aging Patterns for Automatic Age Estimation. In: Proceedings of ACM Conference on Multimedia, Santa Barbara, CA, pp. 307–316 (2006)
Gonzalez-Ulloa, M., Flores, E.S.: Senility of the Face – Basic Study to Understand the Causes and Effects. Plastic and Reconstructive Surgery 36, 239–246 (1965)
Hayashi, J., Yasumoto, M., Ito, H., Koshimizu, H.: A Method for Estimating and Modeling Age and Gender Using Facial Image Processing. In: Proceedings of Seventh International Conference on Virtual Systems and Multimedia, Berkeley, CA, pp. 439–448 (2001)
Kwon, Y., Lobo, N.: Age Classification from Facial Images. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, pp. 762–767 (1994)
Lanitis, A., Taylor, C., Cootes, T.: Toward Automatic Simulation of Aging Effects on Face Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 442–455 (2002)
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
NIST.: The FERET Database (2001), http://www.itl.nist.gov/iad/humanid/feret/
Ojala, T., Pietikäinen, M., Harwood, D.: A Comparative Study of Texture Measures with Classification Based on Feature Distributions. Pattern Recognition 29, 51–59 (1996)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 4(7), 971–987 (2002)
Ramanathan, N., Chellappa, R.: Modeling Age Progression in Young Faces. In: Proceedings of IEEE Computer Vision and Pattern Recognition, New York, vol. 1, pp. 387–394 (2006)
Suo, J., Wu, T., Zhu, S., Shan, S., Chen, X., Gao, W.: Design Sparse Features for Age Estimation Using Hierarchical Face Model. In: Proceedings of IEEE Conference on Automatic Face and Gesture Recognition, Amsterdam, Netherland, pp. 1–6 (2008)
Xiong, X., De la Torre Frade, F.: Supervised Descent Method and Its Applications to Face Alignment. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Portland, OR, pp. 532–539 (2013)
Viola, P., Jones, M.: Rapid Object Detection Using a Boosted Cascade of Simple Features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Kauai, HI, vol. 1, pp. 511–518 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Ali, N., Lin, CF., Hsiung, YS., Tsai, YC., Fuh, CS. (2014). Age and Gender Estimation Using Shifting and Re-scaling of Local Regions. In: Cheng, SM., Day, MY. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2014. Lecture Notes in Computer Science(), vol 8916. Springer, Cham. https://doi.org/10.1007/978-3-319-13987-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-13987-6_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13986-9
Online ISBN: 978-3-319-13987-6
eBook Packages: Computer ScienceComputer Science (R0)