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Age Group Classification in the Wild with Deep RoR Architecture

Citation Author(s):
Ke Zhang, Liru Guo, Ce Gao, Zhenbing Zhao, Miao Sun, Xingfang Yuan
Submitted by:
Ke Zhang
Last updated:
12 September 2017 - 10:21pm
Document Type:
Presentation Slides
Document Year:
2017
Event:
Presenters:
Ke Zhang
Paper Code:
1598
 

Automatically predicting age group from face images acquired in unconstrained conditions is an important and challenging task in many real-world applications. Nevertheless, the conventional methods with manually-designed features on in-the-wild benchmarks are unsatisfactory because of incompetency to tackle large variations in unconstrained images.
In this paper, we propose a new CNN based method for age group classification leveraging Residual Networks of Residual Networks (RoR), which exhibits better optimization ability for age group classification than other CNN architectures. Moreover, two modest mechanisms based on observation of the characteristics of age group are presented to further improve the performance of age estimation. Our experiments illustrate the effectiveness of RoR method for age estimation in the wild, where it achieves better performance than other CNN methods. Finally, the Pre-RoR-58+SD with two mechanisms achieves new state-of-the-art results on Adience benchmark.

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