Abstract
Facial aging effects can be perceived in two main forms; the first one is the growth related transformations and the second one is the textural variations. Therefore, in order to generate an efficient age classifier, both shape and texture information should be used together. In this work, we present an age estimation system that uses the fusion of geometric features (ratios of distance values between facial landmark points) and textural features (filter responses of the face image pixel values). First the probabilities of a face image belonging to each overlapping age groups are calculated by a group of classifiers. Then an interpolation based technique is used to produce the final estimated age. Many different textural features and geometric features were compared in this study. The results of the experiments show that the fusion with the geometric features increases the performance of the textural features and the highest age estimation rates are obtained using the fusion of Local Gabor Binary Patterns and Geometric features with overlapping age groups.
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References
Ahonen, T., Hadid, A., Pietikäinen, M.: Face Recognition with Local Binary Patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)
Bhuiyan, A., Liu, C.H.: On face recognition using gabor filters. World Academy of Science, Engineering and Technology (2007)
Burt, D.M., Perrett, D.I.: Perception of age in adult caucasian male faces: Computer graphic manipulation of shape and colour information. Proceedings of the Royal Society of London. Series B: Biological Sciences 259(1355), 137–143 (1995)
DeCarlo, D., Metaxas, D., Stone, M.: An anthropometric face model using variational techniques. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1998, pp. 67–74. ACM, New York (1998)
Ekenel, H., Fischer, M., Tekeli, E., Stiefelhagen, R., Ercil, A.: Local binary pattern domain local appearance face recognition. In: IEEE 16th Conference on Signal Processing, Communication and Applications, SIU 2008, pp. 1–4 (2008)
FGNET: The FG-NET Aging Database (2010), http://www.fgnet.rsunit.com/
Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156 (1996)
Fu, Y., Guo, G., Huang, T.: Age synthesis and estimation via faces: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(11), 1955–1976 (2010)
Gao, F., Ai, H.: Face Age Classification on Consumer Images with Gabor Feature and Fuzzy LDA Method. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 132–141. Springer, Heidelberg (2009)
Geng, X., Hua Zhou, Z., Smith-Miles, K.: Automatic age estimation based on facial aging patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 2234–2240 (2007)
Guo, G., Fu, Y., Dyer, C., Huang, T.: 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., Mu, G., Fu, Y., Huang, T.: Human age estimation using bio-inspired features. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 112–119 (2009)
Kilinc, M., Akgul, Y.S.: Human age estimation via geometric and textural features. In: Proc. International Conference on Computer Vision Theory and Applications (VISAPP), vol. 1, pp. 531–538 (2012)
Kwon, Y.H., Lobo, N.D.V.: Age classification from facial images. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 762–767 (1999)
Lanitis, A., Draganova, C., Christodoulou, C.: Comparing different classifiers for automatic age estimation. IEEE Trans. Systems, Man, Cybernetics Part B 34(1), 621–628 (2004)
Ricanek Jr., K., Tesafaye, T.: MORPH: A longitudinal image database of normal adult age-progression. In: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, FGR 2006, Washington, DC, USA, pp. 341–345 (2006)
Ramanathan, N., Chellappa, R.: Modeling age progression in young faces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 387–394 (2006)
Ramanathan, N., Chellappa, R.: Modeling shape and textural variations in aging faces. In: 8th IEEE International Conference on Automatic Face Gesture Recognition, FG 2008, pp. 1–8 (2008)
Shan, S., Gao, W., Chang, Y., Cao, B., Yang, P.: Review the Strength of Gabor Features for Face Recognition from the Angle of Its Robustness to Mis-Alignment. In: International Conference on Pattern Recognition, vol. 1, pp. 338–341 (2004)
Yan, S., Zhou, X., Liu, M., Hasegawa-Johnson, M., Huang, T.: Regression from patch-kernel. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008)
Yang, Z., Ai, H.: Demographic Classification with Local Binary Patterns. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 464–473. Springer, Heidelberg (2007)
Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local gabor binary pattern histogram sequence (lgbphs): a novel non-statistical model for face representation and recognition. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 1, pp. 786–791 (2005)
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Kilinc, M., Akgul, Y.S. (2013). Automatic Human Age Estimation Using Overlapped Age Groups. In: Csurka, G., Kraus, M., Laramee, R.S., Richard, P., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics. Theory and Application. Communications in Computer and Information Science, vol 359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38241-3_21
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DOI: https://doi.org/10.1007/978-3-642-38241-3_21
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