Abstract
This paper presents a novel age transformation algorithm to handle the challenge of facial aging in face recognition. The proposed algorithm registers the gallery and probe face images in polar coordinate domain and minimizes the variations in facial features caused due to aging. The efficacy of the proposed age transformation algorithm is validated using 2D log polar Gabor based face recognition algorithm on a face database that comprises of face images with large age progression. Experimental results show that the proposed algorithm significantly improves the verification and identification performance.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Burt, D.M., Perrett, D.I.: Perception of age in adult caucasian male faces: computer graphic manipulation of shape and colour information. In: Proceedings of Royal Society London, Series B 259, 137–143 (1995)
Tiddeman, B., Burt, M., Perrett, D.: Prototyping and transforming facial textures for perception research. IEEE Computer Graphics and Applications 21(5), 42–50 (2001)
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)
Lanitis, A.: On the significance of different facial parts for automatic age estimation. In: Proceedings of International Conference on Digital Signal Processing, vol. 2, pp. 1027–1030 (2002)
Lanitis, A., Draganova, C., Christodoulou, C.: Comparing different classifiers for automatic age estimation. IEEE Transactions on Systems, Man, and Cybernactics 34(1), 621–628 (2004)
Gandhi, M.: A method for automatic synthesis of aged human facial images, Masters Thesis, Department of Electrical and Computer Engineering, McGill University (2004)
Wang, J., Shang, Y., Su, G., Lin, X.: Age simulation for face recognition. In: Proceedings of International Conference on Pattern Recognition, pp. 913–916 (2006)
Ramanathan, N., Chellappa, R.: Face verification across age progression. IEEE Transactions on Image Processing 15(11), 3349–3362 (2006)
Ramanathan, N., Chellappa, R.: Modeling age progression in young faces. Proceedings of IEEE Computer Vision and Pattern Recognition 1, 387–394 (2006)
Singh, R., Vatsa, M., Noore, A.: Face recognition with disguise and single gallery images. Image and Vision Computing (2007)
FG-Net Aging Database, http://www.fgnet.rsunit.com/
Kovesi, P.D.: Image features from phase congruency, Videre: Journal of Computer Vision Research, MIT Press 1(3) (1999)
Singh, R., Vatsa, M., Noore, A.: Improving verification accuracy by synthesis of locally enhanced biometric images and deformable model. Signal Processing 87(11), 2746–2764 (2007)
Gonzalez, R.C., Woods, R.E.: Digital image processing, 2nd edn. Prentice Hall, Englewood Cliffs (2002)
Li, S.Z., Jain, A.K.: Handbook of face recognition. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Singh, R., Vatsa, M., Noore, A., Singh, S.K. (2007). Age Transformation for Improving Face Recognition Performance. In: Ghosh, A., De, R.K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2007. Lecture Notes in Computer Science, vol 4815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77046-6_71
Download citation
DOI: https://doi.org/10.1007/978-3-540-77046-6_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77045-9
Online ISBN: 978-3-540-77046-6
eBook Packages: Computer ScienceComputer Science (R0)