Abstract
Estimating age from a single facial image is a classic and challenging topic in computer vision. One of its most intractable issues is label ambiguity, i.e., face images from adjacent age of the same person are often indistinguishable. Some existing methods adopt distribution learning to tackle this issue by exploiting the semantic correlation between age labels. Actually, most of them set a fixed value to the variance of Gaussian label distribution for all the images. However, the variance is closely related to the correlation between adjacent ages and should vary across ages and identities. To model a sample-specific variance, in this paper, we propose an adaptive variance based distribution learning (AVDL) method for facial age estimation. AVDL introduces the data-driven optimization framework, meta-learning, to achieve this. Specifically, AVDL performs a meta gradient descent step on the variable (i.e. variance) to minimize the loss on a clean unbiased validation set. By adaptively learning proper variance for each sample, our method can approximate the true age probability distribution more effectively. Extensive experiments on FG-NET and MORPH II datasets show the superiority of our proposed approach to the existing state-of-the-art methods.
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References
Andrychowicz, M., et al.: Learning to learn by gradient descent by gradient descent. In: Advances in Neural Information Processing Systems, pp. 3981–3989 (2016)
Antipov, G., Baccouche, M., Berrani, S.A., Dugelay, J.L.: Effective training of convolutional neural networks for face-based gender and age prediction. Pattern Recogn. 72, 15–26 (2017)
Cao, D., Zhu, X., Huang, X., Guo, J., Lei, Z.: Domain balancing: face recognition on long-tailed domains. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5671–5679 (2020)
Chang, K.Y., Chen, C.S., Hung, Y.P.: Ordinal hyperplanes ranker with cost sensitivities for age estimation. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 585–592. IEEE (2011)
Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013)
Duan, M., Li, K., Li, K.: An ensemble CNN2ELM for age estimation. IEEE Trans. Inf. Forensics Secur. 13(3), 758–772 (2017)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1126–1135 (2017). JMLR.org
Gao, B.B., Xing, C., Xie, C.W., Wu, J., Geng, X.: Deep label distribution learning with label ambiguity. IEEE Trans. Image Proc. 26(6), 2825–2838 (2017)
Gao, B.B., Zhou, H.Y., Wu, J., Geng, X.: Age estimation using expectation of label distribution learning. In: IJCAI, pp. 712–718 (2018)
Geng, X.: Label distribution learning. IEEE Trans. Knowl. Data Eng. 28(7), 1734–1748 (2016)
Geng, X., Wang, Q., Xia, Y.: Facial age estimation by adaptive label distribution learning. In: 2014 22nd International Conference on Pattern Recognition, pp. 4465–4470. IEEE (2014)
Geng, X., Xia, Y.: Head pose estimation based on multivariate label distribution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1837–1842 (2014)
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)
Guo, G., Mu, G.: Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression. In: CVPR 2011, pp. 657–664. IEEE (2011)
Guo, G., Mu, G.: Joint estimation of age, gender and ethnicity: CCA vs. PLS. In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–6. IEEE (2013)
Guo, J., Zhu, X., Zhao, C., Cao, D., Lei, Z., Li, S.Z.: Learning meta face recognition in unseen domains. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6163–6172 (2020)
Guo, Yandong., Zhang, Lei., Hu, Yuxiao., He, Xiaodong, Gao, Jianfeng: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, Bastian, Matas, Jiri, Sebe, Nicu, Welling, Max (eds.) ECCV 2016. LNCS, vol. 9907, pp. 87–102. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_6
Han, H., Otto, C., Liu, X., Jain, A.K.: Demographic estimation from face images: human vs. machine performance. IEEE Trans. Pattern Anal. Mach. Intell. 37(6), 1148–1161 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hou, P., Geng, X., Huo, Z.W., Lv, J.Q.: Semi-supervised adaptive label distribution learning for facial age estimation. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Li, P., Hu, Y., Wu, X., He, R., Sun, Z.: Deep label refinement for age estimation. Pattern Recogn. 100, 107178 (2020)
Li, W., Lu, J., Feng, J., Xu, C., Zhou, J., Tian, Q.: BridgeNet: a continuity-aware probabilistic network for age estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1145–1154 (2019)
Liu, Z., et al.: Semantic alignment: finding semantically consistent ground-truth for facial landmark detection. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3462–3471 (2019)
Niu, Z., Zhou, M., Wang, L., Gao, X., Hua, G.: Ordinal regression with multiple output CNN for age estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4920–4928 (2016)
Pan, H., Han, H., Shan, S., Chen, X.: Mean-variance loss for deep age estimation from a face. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5285–5294 (2018)
Panis, G., Lanitis, A., Tsapatsoulis, N., Cootes, T.F.: Overview of research on facial ageing using the FG-NET ageing database. IET Biometrics 5(2), 37–46 (2016)
Ren, M., et al.: Meta-learning for semi-supervised few-shot classification. arXiv preprint arXiv:1803.00676 (2018)
Ren, M., Zeng, W., Yang, B., Urtasun, R.: Learning to reweight examples for robust deep learning. arXiv preprint arXiv:1803.09050 (2018)
Ricanek, K., Tesafaye, T.: Morph: a longitudinal image database of normal adult age-progression. In: 7th International Conference on Automatic Face and Gesture Recognition (FGR 2006), pp. 341–345. IEEE (2006)
Rothe, R., Timofte, R., Van Gool, L.: DEX: deep expectation of apparent age from a single image. In: The IEEE International Conference on Computer Vision (ICCV) Workshops, December 2015
Rothe, R., Timofte, R., Van Gool, L.: Deep expectation of real and apparent age from a single image without facial landmarks. Int. J. Comput. Vision 126(2–4), 144–157 (2018)
Shen, W., Guo, Y., Wang, Y., Zhao, K., Wang, B., Yuille, A.L.: Deep regression forests for age estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2304–2313 (2018)
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)
Tan, Z., Yang, Y., Wan, J., Guo, G., Li, S.Z.: Deeply-learned hybrid representations for facial age estimation. In: IJCAI, pp. 3548–3554 (2019)
Tan, Z., Yang, Y., Wan, J., Wan, H., Guo, G., Li, S.: Attention-based pedestrian attribute analysis. IEEE Trans. Image Process. PP, 1–1 (2019). https://doi.org/10.1109/TIP.2019.2919199
Thrun, S., Pratt, L.: Learning to Learn. Springer Science & Business Media (2012)
Vanschoren, J.: Meta-learning: a survey. arXiv preprint arXiv:1810.03548 (2018)
Wang, G., Han, H., Shan, S., Chen, X.: Cross-domain face presentation attack detection via multi-domain disentangled representation learning. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020
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)
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: IJCAI, vol. 5, p. 7 (2018)
Yang, X., et al.: Deep label distribution learning for apparent age estimation. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 102–108 (2015)
Yi, Dong., Lei, Zhen, Li, Stan Z.: Age estimation by multi-scale convolutional network. In: Cremers, Daniel, Reid, Ian, Saito, Hideo, Yang, Ming-Hsuan (eds.) ACCV 2014. LNCS, vol. 9005, pp. 144–158. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16811-1_10
Zhang, C., Liu, S., Xu, X., Zhu, C.: C3AE: exploring the limits of compact model for age estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12587–12596 (2019)
Zhao, X., Liang, X., Zhao, C., Tang, M., Wang, J.: Real-time multi-scale face detector on embedded devices. Sensors 19(9), 2158 (2019)
Zhu, B., Chen, Y., Tang, M., Wang, J.: Progressive cognitive human parsing. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Zhu, B., Chen, Y., Wang, J., Liu, S., Zhang, B., Tang, M.: Fast deep matting for portrait animation on mobile phone. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 297–305 (2017)
Acknowledgments
This work was supported by Key-Area Research and Development Program of Guangdong Province (No. 2019B010153001), National Natural Science Foundation of China (No. 61772527,61806200,61976210), China Postdoctoral science Foundation (No. 2019M660859), Open Project of Key Laboratory of Ministry of Public Security for Road Traffic Safety (No. 2020ZDSYSKFKT04).
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Wen, X. et al. (2020). Adaptive Variance Based Label Distribution Learning for Facial Age Estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12368. Springer, Cham. https://doi.org/10.1007/978-3-030-58592-1_23
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