Abstract
Although remarkable progresses have been made in the field of face recognition, the cross-age problem is still a huge challenge. The cross-age problem is mainly reflected in the fact that in addition to the unique identity features of each person, facial features also contain age features changing during aging. To address this problem, we propose a novel cross-age face recognition framework based on dual attention mechanism which combines residual-attention mechanism and self-attention mechanism. The introduction of attention mechanism makes the model focus more on identity features, ignoring the influence of age features. Extensive experiments are conducted on two well-known face aging datasets (MORPH and CACD) to show that the proposed method achieves notable improvement over state-of-the-art algorithms.
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Acknowledgements
This research work is funded by the National Nature Science Foundation of China under Grant 61971283 and 2019 Industrial Internet Innovation Development Project of Ministry of Industry and Information Technology of P.R. China “Comprehensive Security Defense Platform Project for Industrial/Enterprise Networks” under Grant SGFJ0000HLHZ2000034.
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Wang, J., Li, S., Luo, F. (2021). Cross-Age Face Recognition Using Deep Learning Model Based on Dual Attention Mechanism. In: Liang, Q., Wang, W., Liu, X., Na, Z., Li, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2020. Lecture Notes in Electrical Engineering, vol 654. Springer, Singapore. https://doi.org/10.1007/978-981-15-8411-4_251
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DOI: https://doi.org/10.1007/978-981-15-8411-4_251
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