Deep cost-sensitive and order-preserving feature learning for cross-population age estimation

Li, K. and Junliang, X. and Chi, S. and Weiming, H. and Zhang, Y. and Maybank, Stephen J. (2018) Deep cost-sensitive and order-preserving feature learning for cross-population age estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, 2018: CVPR 2018, 18-22 Jun 2018, Salt Lake City, U.S..

Abstract

Facial age estimation from a face image is an important yet very challenging task in computer vision, since humans with different races and/or genders, exhibit quite different patterns in their facial aging processes. To deal with the influence of race and gender, previous methods perform age estimation within each population separately. In practice, however, it is often very difficult to collect and label suf- ficient data for each population. Therefore, it would be helpful to exploit an existing large labeled dataset of one (source) population to improve the age estimation perfor- mance on another (target) population with only a small la- beled dataset available. In this work, we propose a Deep Cross-Population (DCP) age estimation model to achieve this goal. In particular, our DCP model develops a two- stage training strategy. First, a novel cost-sensitive multi- task loss function is designed to learn transferable aging features by training on the source population. Second, a novel order-preserving pair-wise loss function is designed to align the aging features of the two populations. By doing so, our DCP model can transfer the knowledge encoded in the source population to the target population. Extensive experiments on the two of the largest benchmark datasets show that our DCP model outperforms several strong base- line methods and many state-of-the-art methods.

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