Abstract
Face aging simulation is a very complex and challenging task and interests many researchers in the fields of psychology, computer graphics and computer vision due to its widely applications. In this paper, we propose a multi-layer coarse-to-fine face representation and aging simulation and animation algorithm. In the coarse layer, we build a global statistical appearance model for representation and faces are aged based on the learned age trajectory in the appearance space. In the mid layer, we learned a set of age specific coupled dictionaries and the faces are represented and aged via the sparse representation on the learned dictionary. At the fine layer, we sample a lot of patches of facial components and skin zones from images of each age group and use them as the dictionaries to simulate the aging effects of the facial components and wrinkles. We collect a database of 10,050 Chinese passport-type images with different ages for the learning and aging simulation. Experimental results demonstrate the effectiveness of the proposed method.
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
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 54(11), 4311–4322
Boissieux, L., Kiss, G., Thalmann, N.M., Kalra, P.: Simulation of skin aging and wrinkles with cosmetics insight. In: proceedings of the EUROGRAPHICS Workshop, pp. 15–27 (2000)
Burt, D.M., Perrett, D.I.: Perception of age in adult caucasian male faces: Computer graphic manipulation of shape and color information. Proceedings of the Royal Society of London B 259, 137–143 (1995)
Cootes, T., Taylor, C.: Statistical models of appearance for computer vision. Technical Report, The University of Manchester School of Medicine (2004)
Dayan, N.: Skin aging handbook: An integrated approach to biochemistry and product development. Andrew William Press (2008)
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)
Fu, Y., Zheng, N.: M-face: An appearance-based photorealistic model for multiple facial attributes rendering. IEEE Transactions on Circuits and Systems for Video Technology 16(7), 830–842 (2006)
Golovinskiy, A., Matusik, W., Pfister, H., Rusinkiewicz, S., Funkhouser, T.: A statistical model for synthesis of detailed facial geometry. In: ACM SIGGRAPH, pp. 1025–1034 (2006)
Scandrett née Hill, C., Solomon, C., Gibson, S.: A person-specific, rigorous aging model of the human face. Pattern Recognition Letters 27(15), 1776–1787 (2006)
Hu, M.K.: Visual pattern recognition by moment invariants. IRE Transactions on Information Theory 8(2), 179–187 (2002)
Hubball, D., Chen, M., Grant, P.W.: Image-based aging using evolutionary computing. In: EUROGRAPHICS, Computer Graphics Forum, vol. 27, pp. 607–616 (2008)
Hutton, T.J., Buxton, B.F., Hammond, P., Potts, H.W.W.: Estimating average growth trajectories in shape-space using kernel smoothing. IEEE Transactions on Medical Imaging 22(6), 747–753 (2003)
Jiang, F.Y., Wang, Y.H.: Facial aging simulation based on super-resolution in tenson space. In: International Conference on Image Processing, pp. 1648–1651 (2008)
Lanitis, A.: Comparative evaluation of automatic age-progression methodologies. EURASIP Journal on Advances in Signal Processing 2008, 1–10 (2008)
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), 422–455
Lee, W.S., Wu, Y., Thalmann, N.M.: Cloneing and aging in a vr family. In: IEEE Conference on Virtual Reality, pp. 61–68 (1999)
Liang, Y.X., Li, C.R., Yue, H.Q., Luo, Y.Y.: Age simulation in young face images. In: International Conference on Bioinformatics and Biomedical Engineering, pp. 494–497 (2007)
Ling, H., Soatto, S., Ramanathan, N., Jacobs, D.W.: Study of face recognition as people age. In: IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)
Liu, C., Shum, H., Freeman, W.: Face hallucination: Theory and practice. International Journal of Computer Vision 75(1), 115–134 (2007)
Ojala, T., pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
O’Toole, Price, T., Vetter, T., Barlett, J.C., Blanz, V.: 3D shape and 2D surface textures of human faces: The role of averages in attractiveness and age. Image and Vision Computing 18(1), 9–19 (1999)
O’Toole, Vetter, T., Volz, H., Salter, E.: Three-dimensional caricatures of human heads: Distinctiveness and the perception of facial age. Perception 26, 719–732 (1997)
Park, U., Tong, Y., Jain, A.: Age-invariant face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(5), 947–954 (2010)
Pittenger, J.B., Shaw, R.E., Mark, L.S.: Perceptual information for the age level of faces as a higher order invariant of growth. Journal of Experimental Psychology: Human Perception and Performance 5(3), 478–493 (1979)
Ramanathan, N., Chellappa, R., Biswas, S.: Modeling age progression in young faces. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 387–394 (2006)
Ramanathan, N., Chellappa, R., Biswas, S.: Modeling shape and textural variations in aging faces. In: 8th IEEE International Conference on Automatric Face and Gesture Recognition, pp. 1–8 (2008)
Ramanathan, N., Chellappa, R., Biswas, S.: Age progression in human faces: A survey. Journal of Visual Languages and Computing 15, 3349–3361 (2009)
Scherbaum, K., Sunkel, M., Seidel, H.P., Blanz, V.: Prediction of individual non-linear aging trajectories of faces. In: EUROGRAPHICS, Computer Graphics Forum, vol. 26 (2007)
Singh, R., Vatsa, M., Noore, A., Singh, S.K.: Age transformation for improving face recognition. In: Proceedings of the 2nd international conference on Pattern recognition and machine intelligence, pp. 576–583 (2007)
Suo, J.L., Min, F., Zhu, S.C., Shan, S.G., Chen, X.L.: A multi-resolution dynamic model for face aging simulation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Suo, J.L., Zhu, S.C., Chen, X.L.: A compositional and dynamic model for face aging. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 358–401 (2010)
Tiddeman, B., Burt, D.M., Perrett, D.I.: Prototyping and transforming facial textures for perception research. IEEE Computer Graphics and Applications 21(5), 42–50 (2001)
Todd, J.T., Mark, L.S., Shaw, R.E., Pittenger, J.B.: The perception of human growth. Scientific American 242(2), 132–144 (1980)
Wang, J.N., Ling, C.J.: Artificial aging of faces by support vector machines. In: Advances in Artifical Intelligence, pp. 499–503 (2006)
Wu, Y., Kalra, P., Moccozet, L., Thalmann, N.: Simulating wrinkles and skin aging. The Visual Computer 15(4), 183–198 (1999)
Yang, J., Tang, H., Ma, Y., Huang, T.: Face hallucination via sparse coding. In: 15th IEEE International Conference on Image Processing, pp. 1264–1267. IEEE, New York (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Liang, Y., Xu, Y., Liu, L., Liao, S., Zou, B. (2011). A Multi-layer Model for Face Aging Simulation. In: Pan, Z., Cheok, A.D., Müller, W. (eds) Transactions on Edutainment VI. Lecture Notes in Computer Science, vol 6758. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22639-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-22639-7_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22638-0
Online ISBN: 978-3-642-22639-7
eBook Packages: Computer ScienceComputer Science (R0)