Age-Invariant Face Recognition Based on Self-Supervised Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-46749-3_2
Reference16 articles.
1. Arora, R., Kaur, P., Kaur, E.D.: Age invariant face recogntion using stacked autoencoder deep neural network. In: 2020 International Conference on Intelligent Engineering and Management (ICIEM), pp. 358–363 (2020). https://doi.org/10.1109/ICIEM48762.2020.9160113
2. Baruni, K., Mokoena, N., Veeraragoo, M., Holder, R.: Age invariant face recognition methods: A review. In: 2021 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1657–1662 (2021). https://doi.org/10.1109/CSCI54926.2021.00317
3. Bijarnia, S., Singh, P.: Age invariant face recognition using minimal geometrical facial features. In: Choudhary, R.K., Mandal, J.K., Auluck, N., Nagarajaram, H.A. (eds.) Advanced Computing and Communication Technologies, pp. 71–77. Springer Singapore, Singapore (2016)
4. Du, J.X., Zhai, C.M., Ye, Y.Q.: Face aging simulation based on nmf algorithm with sparseness constraints. In: Huang, D.S., Gan, Y., Gupta, P., Gromiha, M.M. (eds.) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, pp. 516–522. Springer Berlin Heidelberg, Berlin, Heidelberg (2012)
5. Gomez-Trenado, G., Lathuilière, S., Mesejo, P., Cordón, Ó.: Custom structure preservation in face aging. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022, pp. 565–580. Springer, Cham (2022)
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