Deep label refinement for age estimation
作者：
Highlights：
• We propose a Label Refinery Network (LRN) with two concurrent processes: label distribution refinery and slack regression refinery.
• The proposed label distribution refinery adaptively estimates the age distributions without the strong assumptions about the form of label distribution. Benefiting from the constant refinery of the learning results, label distribution refinery generates more precise label distributions.
• To further utilize the correlations among different age labels, we introduce regression to assist label distribution refinery. Besides, we introduce a slack term to further convert the discrete age label regression to the continuous age interval regression.
• We evaluate the effectiveness of the proposed LRN on three age estimation benchmarks and consistently obtain the state-of-the-art results.
摘要
•We propose a Label Refinery Network (LRN) with two concurrent processes: label distribution refinery and slack regression refinery.•The proposed label distribution refinery adaptively estimates the age distributions without the strong assumptions about the form of label distribution. Benefiting from the constant refinery of the learning results, label distribution refinery generates more precise label distributions.•To further utilize the correlations among different age labels, we introduce regression to assist label distribution refinery. Besides, we introduce a slack term to further convert the discrete age label regression to the continuous age interval regression.•We evaluate the effectiveness of the proposed LRN on three age estimation benchmarks and consistently obtain the state-of-the-art results.
论文关键词：Age estimation,Deep learning,Convolutional neural networks,Label distribution learning
论文评审过程：Received 29 November 2018, Revised 24 November 2019, Accepted 15 December 2019, Available online 24 December 2019, Version of Record 27 December 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.107178
