Abstract
Facial age estimation is an important yet very challenging problem in computer vision. To improve the performance of facial age estimation, we first formulate a simple standard baseline and build a much strong one by collecting the tricks in pre-training, data augmentation, model architecture, and so on. Compared with the standard baseline, the proposed one significantly decreases the estimation errors. Moreover, long-tailed recognition has been an important topic in facial age datasets, where the samples often lack on the elderly and children. To train a balanced age estimator, we propose a two-stage training method named Long-tailed Age Estimation (LAE), which decouples the learning procedure into representation learning and classification. The effectiveness of our approach has been demonstrated on the dataset provided by organizers of Guess The Age Contest 2021.
Z. Bao and Z. Tan—Co-First Author.
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References
Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: VGGFace2: a dataset for recognising faces across pose and age. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 67–74. IEEE (2018)
Carletti, V., Greco, A., Percannella, G., Vento, M.: Age from faces in the deep learning revolution. IEEE Trans. Pattern Anal. Mach. Intell. 42(9), 2113–2132 (2019)
Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: RandAugment: practical automated data augmentation with a reduced search space. In: CVPRW (2020)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Gao, B.B., Zhou, H.Y., Wu, J., Geng, X.: Age estimation using expectation of label distribution learning. In: IJCAI, pp. 712–718 (2018)
Greco, A., Saggese, A., Vento, M., Vigilante, V.: Effective training of convolutional neural networks for age estimation based on knowledge distillation. Neural Comput. Appl., 1–16 (2021)
Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 87–102. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_6
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. arXiv preprint arXiv:1910.09217 (2019)
Niu, Z., Zhou, M., Wang, L., Gao, X., Hua, G.: Ordinal regression with multiple output CNN for age estimation. In: CVPR (2016)
Othmani, A., Taleb, A.R., Abdelkawy, H., Hadid, A.: Age estimation from faces using deep learning: a comparative analysis. Comput. Vis. Image Underst. 196, 102961 (2020)
Punyani, P., Gupta, R., Kumar, A.: Neural networks for facial age estimation: a survey on recent advances. Artif. Intell. Rev. 53(5), 3299–3347 (2019). https://doi.org/10.1007/s10462-019-09765-w
Ren, J., et al.: Balanced meta-softmax for long-tailed visual recognition. arXiv preprint arXiv:2007.10740 (2020)
Rothe, R., Timofte, R., Van Gool, L.: DEX: deep expectation of apparent age from a single image. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 10–15 (2015)
Shen, L., Lin, Z., Huang, Q.: Relay backpropagation for effective learning of deep convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 467–482. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_29
Smith, L.N., Topin, N.: Super-convergence: very fast training of neural networks using large learning rates. In: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, vol. 11006, p. 1100612. International Society for Optics and Photonics (2019)
Tan, M., Le, Q.V.: EfficientNetV2: smaller models and faster training. arXiv preprint arXiv:2104.00298 (2021)
Tan, Z., Wan, J., Lei, Z., Zhi, R., Guo, G., Li, S.Z.: Efficient group-n encoding and decoding for facial age estimation. IEEE TPAMI (2018)
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)
Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5265–5274 (2018)
Zeng, X., Huang, J., Ding, C.: Soft-ranking label encoding for robust facial age estimation. IEEE Access (2020)
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23, 1499–1503 (2016)
Acknowledgements
This work was supported by the Chinese National Natural Science Foundation Projects #61961160704, #61876179, the External cooperation key project of Chinese Academy Sciences # 173211KYSB20200002, the Key Project of the General Logistics Department Grant No. AWS17J001, Science and Technology Development Fund of Macau (No. 0010/2019/AFJ, 0008/2019/A1 0025/2019/A-KP0019/2018/ASC).
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Bao, Z. et al. (2021). LAE : Long-Tailed Age Estimation. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13053. Springer, Cham. https://doi.org/10.1007/978-3-030-89131-2_28
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