Abstract
With the rapid development of deep learning, face recognition also finds its improving dramatically. However, facial change is still a main effect to the accuracy of recognition, as some complex factors like age-invariant, health state and emotion, are hard to model. Unlike some previous methods decomposing facial features into age-related and identity-related parts, we propose an innovative end-to-end method that introduces a deformable convolution into a deep learning discriminant model and automatically learns how the facial characteristics changes over time, and test its effectiveness on multiple data sets.
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This work was supported by the National Key Research and Development Project of China under Grant 2016YFB0801003.
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Zhan, H., Li, S., Guo, H. (2020). A Deep Deformable Convolutional Method for Age-Invariant Face Recognition. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_245
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DOI: https://doi.org/10.1007/978-981-13-9409-6_245
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