Abstract
In the last five years, biologically inspired features (BIF) always held the state-of-the-art results for human age estimation from face images. Recently, researchers mainly put their focuses on the regression step after feature extraction, such as support vector regression (SVR), partial least squares (PLS), canonical correlation analysis (CCA) and so on. In this paper, we apply convolutional neural network (CNN) to the age estimation problem, which leads to a fully learned end-to-end system can estimate age from image pixels directly. Compared with BIF, the proposed method has deeper structure and the parameters are learned instead of hand-crafted. The multi-scale analysis strategy is also introduced from traditional methods to the CNN, which improves the performance significantly. Furthermore, we train an efficient network in a multi-task way which can do age estimation, gender classification and ethnicity classification well simultaneously. The experiments on MORPH Album 2 illustrate the superiorities of the proposed multi-scale CNN over other state-of-the-art methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The detailed evaluation protocols and facial landmarks can be downloaded from http://www.cbsr.ia.ac.cn/users/dyi/agr.html.
References
Kwon, Y.H., da Vitoria Lobo, N.: Age classification from facial images. In: 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Proceedings CVPR 1994, pp. 762–767 (1994)
Lanitis, A., Draganova, C., Christodoulou, C.: Comparing different classifiers for automatic age estimation. IEEE Trans. Syst. Man Cybern. B: Cybern. 34, 621–628 (2004)
Guo, G., Mu, G.: Joint estimation of age, gender and ethnicity: CCA vs. PLS. In: FG, pp. 1–6 (2013)
Rawls, A.W., Ricanek Jr, K.: MORPH: development and optimization of a longitudinal age progression database. In: Fierrez, J., Ortega-Garcia, J., Esposito, A., Drygajlo, A., Faundez-Zanuy, M. (eds.) BioID MultiComm2009. LNCS, vol. 5707, pp. 17–24. Springer, Heidelberg (2009)
Basak, D., Pal, S., Patranabis, D.C.: Support vector regression. Neural Inf. Process. Lett. Rev. 11, 203–224 (2007)
Geladi, P., Kowalski, B.R.: Partial least-squares regression: a tutorial. Anal. Chim. Acta 185, 1–17 (1986)
Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16, 2639–2664 (2004)
Daugman, J.G.: Complete discrete 2D Gabor transforms by neural networks for image analysis and compression. IEEE Trans. ASSP 36, 1169–1179 (1988)
Yang, M., Zhu, S., Lv, F., Yu, K.: Correspondence driven adaptation for human profile recognition. In: CVPR, pp. 505–512 (2011)
Guo, G., Mu, G., Fu, Y., Huang, T.S.: Human age estimation using bio-inspired features. In: CVPR. pp. 112–119 (2009)
Kwon, Y.H., da Vitoria Lobo, N.: Age classification from facial images. Comput. Vis. Image Underst. 74, 1–21 (1999)
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)
Geng, X., Zhou, Z.H., Smith-Miles, K.: Automatic age estimation based on facial aging patterns. IEEE Trans. Pattern Anal. Mach. Intell. 29, 2234–2240 (2007)
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)
Gunay, A., Nabiyev, V.V.: Automatic age classification with LBP. In: 2008 23rd IEEE International Symposium on Computer and Information Sciences. ISCIS 2008, pp. 1–4 (2008)
Yan, S., Liu, M., Huang, T.S.: Extracting age information from local spatially flexible patches. In: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP 2008, pp. 737–740 (2008)
Fu, Y., Huang, T.S.: Human age estimation with regression on discriminative aging manifold. IEEE Trans. Multimed. 10, 578–584 (2008)
Cao, D., Lei, Z., Zhang, Z., Feng, J., Li, S.Z.: Human age estimation using ranking SVM. In: Zheng, W.-S., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds.) CCBR 2012. LNCS, vol. 7701, pp. 324–331. Springer, Heidelberg (2012)
Herbrich, R., Graepel, T., Obermayer, K.: Large margin rank boundaries for ordinal regression. In: Smola, A.J., et al. (eds.) Advances in Large Margin Classifiers. MIT Press, Cambridge (2000)
Guo, G., Mu, G.: Simultaneous dimensionality reduction and human age estimation via Kernel partial least squares regression. In: CVPR. pp. 657–664 (2011)
Duffner, S.: Face image analysis with convolutional neural networks. Ph.D. thesis (2008)
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1915–1929 (2013)
Chen, D., Cao, X., Wen, F., Sun, J.: Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification. In: CVPR, pp. 3025–3032 (2013)
ul Hussain, S., Wheeler, Napolon, T., Jurie, F.: Face recognition using local quantized patterns. In: Proceedings on British Machine Vision Conference, vol. 1, pp. 52–61 (2012)
Li, S.Z., Yi, D., Lei, Z., Liao, S.: The CASIA NIR-VIS 2.0 face database. In: CVPR Workshops, pp. 348–353 (2013)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models: their training and application. CVGIP: Image Underst. 61, 38–59 (1995)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1106–1114 (2012)
Guo, G., Mu, G.: Human age estimation: What is the influence across race and gender? In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 71–78 (2010)
Friedman, J., Tibshirani, R., Hastie, T.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer, New York (2009)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii (2001)
Geng, X., Yin, C., Zhou, Z.H.: Facial age estimation by learning from label distributions. IEEE Trans. Pattern Anal. Mach. Intell. 35, 2401–2412 (2013)
Acknowledgment
This work was supported by the Chinese National Natural Science Foundation Projects #61105023, #61103156, #61105037, #61203267, #61375037, #61473291, National Science and Technology Support Program Project #2013BAK02B01, Chinese Academy of Sciences Project No. KGZD-EW-102-2, and AuthenMetric R&D Funds. The GPU was donated by NVIDIA.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Yi, D., Lei, Z., Li, S.Z. (2015). Age Estimation by Multi-scale Convolutional Network. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9005. Springer, Cham. https://doi.org/10.1007/978-3-319-16811-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-16811-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16810-4
Online ISBN: 978-3-319-16811-1
eBook Packages: Computer ScienceComputer Science (R0)