Abstract
In recent years, deep learning has become one of the most representative and effective techniques in face recognition. Due to the high expense of labelling data, it is costly to collect a large-scale face dataset with accurate label information. For the tasks without sufficient data, deep models cannot be well trained. Generally, parameters of deep models are usually initialized with a pre-trained model, and then fine-tuned on a small dataset of specific task. However, by straightforward fine-tuning, the final model usually does not generalize well. In this paper, we propose a multi-task deep learning (MTDL) method for face recognition. The superiority of the proposed multi-task method is demonstrated by experiments on LFW and CCFD.
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Acknowledgements
This work was supported by the National Program on Key Basic Research Project under Grant 2013CB329304, the National Natural Science Foundation of China under Grants 61502332, 61432011, 61222210.
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Yuan, J., Ma, W., Zhu, P., Egiazarian, K. (2017). Multi-task Deep Face Recognition. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_20
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