Abstract
Age estimation from face images has attracted much attention due to its favorable of many real-world applications such as video surveillance and social networking. However, most existing studies usually directly extract aging-feature, which ignore the high age-related factors such as race and gender information. In this paper, we propose a joint multi-feature learning method for robust facial age estimation by extensively exploring age-related features. Specifically, we first specially learn the race and gender features from face images, which are two highly related information for age estimation of an individual. Then, we jointly learn the aging-feature by concatenating these race-specific and gender-specific information maps with the original face images. To fully utilize the continuity and the order of age labels, we form a regression-ranking age estimator to predict the final age. Experimental results on three benchmark databases demonstrate the superior performance of our proposed method on facial age estimation in comparison with other state-of-the-art methods.
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
References
Rothe, R., Timofte, R., Van Gool, L.: Deep expectation of real and apparent age from a single image without facial landmarks. Int. J. Comput. Vis. 126(2), 144–157 (2016). https://doi.org/10.1007/s11263-016-0940-3
Niu, Z., Zhou, M,. Wang, L., Gao, X., Hua, G.: Ordinal regression with multiple output CNN for age estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4920–4928 (2016)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4920–4928 (2016)
Guo, G., Mu, G., Fu, Y.: Human age estimation using bio-inspired features. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 112–119 (2009)
Antipov, G., Baccouche, M.: Effective training of convolutional neural networks for face-based gender and age prediction. Pattern Recognit. 72(1), 15–26 (2017)
Pei, W., Dibeklioǧlu, H., Baltrušaitis, T., Tax, D.M.: Attended end-to-end architecture for age estimation from facial expression videos. IEEE Trans. Image Process. 29, 1972–1984 (2019)
Tan, Z., Yang, Y., Wan, J., Guo, G.: Deeply-learned hybrid representations for facial age estimation. In: International Joint Conferences on Artificial Intelligence, pp. 3548–3554 (2009)
Liu, H., Lu, J., Feng, J., Zhou, J.: Group-aware deep feature learning for facial age estimation. Pattern Recognit. 66, 82–94 (2017)
Chen, P.H., Lin, C.J., Schölkopf, B.: A tutorial on v-support vector machines. Appl. Stoch. Models Bus. Ind. 21(2), 111–136 (2005)
Shen, W., Guo, Y., Wang, Y., Zhao, K., Wang, B.: Deep differentiable random forests for age estimation. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 404–419 (2021)
Gunay, A., Nabiyev, V.V.: Automatic age classification with LBP. In: International Symposium on Computer and Information Sciences, vol. 1, pp. 1–4 (2008)
Zhang, C., Liu, S., Xu, X., Zhu, C.: C3AE: exploring the limits of compact model for age estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 12587–12596 (2019)
Agustsson, E., Timofte, R., Van Gool, L.: Anchored regression networks applied to age estimation and super resolution. In: IEEE International Conference on Computer Vision, pp. 1643–1652 (2017)
Geng, X., Yin, C., Zhou, Z.H.: Facial age estimation by learning from label distributions. IEEE Trans. Pattern Anal. Mach. Intell. 35(10), 2401–2412 (2013)
Xie, J.C., Pun, C.M.: Deep and ordinal ensemble learning for human age estimation from facial images. IEEE Trans. Inf. Forensics Secur. 15, 2361–2374 (2020)
Chen, S., Zhang, C., Dong, M., Le, J., Rao, M.: Using ranking-CNN for age estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5183–5192 (2017)
Duan, M., Li, K., Li, K.: An ensemble CNN2ELM for age estimation. IEEE Trans. Inf. Forensics Secur. 13(3), 758–772 (2017)
Fei, L., Zhang, B., Xu, Y., Tian, C., Rida, I., Zhang, D.: Jointly heterogeneous palmprint discriminant feature learning. IEEE Trans. Neural Netw. Learn. Syst. 1–12 (2021). https://doi.org/10.1109/TNNLS.2021.3066381 (2021)
Fei, L., Zhang, B., Zhang, L., Jia, W., Wen, J., Wu, J.: Learning compact multifeature codes for palmprint recognition from a single training image per palm. IEEE Trans. Multimedia 23, 2930–2942 (2021)
Yaman, D., Irem Eyiokur, F., Kemal Ekenel, H.: Multimodal age and gender classification using ear and profile face images. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2414–2421 (2019)
Antipov, G., Baccouche, M., Berrani, S.A., Dugelay, J.L.: Apparent age estimation from face images combining general and children-specialized deep learning models. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 96–104 (2016)
Wang, L., Huang, J., Yin, M., Cai, R., Hao, Z.: Block diagonal representation learning for robust subspace clustering. Inf. Sci. 526, 54–67 (2020)
Yang, T.Y., Huang, Y.H., Lin, Y.Y., Hsiu, P.C., Chuang, Y.Y.: SSR-Net: a compact soft stagewise regression network for age estimation. In: International Joint Conference on Artificial Intelligence, pp. 1078–1084 (2018)
Zheng, D.P., Du, J.X., Fan, W.T., Wang, J., Zhai, C.M.: Deep learning with PCANet for human age estimation. In: Huang, D.-S., Jo, K.-H. (eds.) ICIC 2016, Part II. LNCS, vol. 9772, pp. 300–310. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42294-7_26
Zaghbani, S., Boujneh, N., Bouhlel, M.S.: Age estimation using deep learning. Comput. Electr. Eng. 68, 337–347 (2018)
Li, K., Xing, J., Su, C., Hu, W., Zhang, Y., Maybank, S.: Deep cost-sensitive and order-preserving feature learning for cross-population age estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 399–408 (2018)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part VII. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409 (2018)
Cootes, T.F., Edwards, G.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)
Tan, Z., Wan, J., Lei, Z., Zhi, R., Guo, G., Li, S.Z.: Efficient group-n encoding and decoding for facial age estimation. IEEE Trans. Pattern Anal. Mach. Intell. 40(11), 2610–2623 (2017)
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grants 62176066, 62076086 and 62006059, in part by the Guangzhou Science and technology plan project under Grant 202002030110, in part by the Natural Science Foundation of Guangdong Province under Grant 2019A1515011811.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Deng, Y. et al. (2022). Joint Multi-feature Learning for Facial Age Estimation. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13188. Springer, Cham. https://doi.org/10.1007/978-3-031-02375-0_38
Download citation
DOI: https://doi.org/10.1007/978-3-031-02375-0_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-02374-3
Online ISBN: 978-3-031-02375-0
eBook Packages: Computer ScienceComputer Science (R0)