using multiple losses for accurate facial age estimation

TL;DR

This paper introduces Age-Granularity-Net, a novel framework combining multiple classification and regression losses to improve facial age estimation accuracy over traditional classification methods.

Contribution

It proposes a new multi-loss approach that leverages different class granularities for more accurate age estimation, outperforming existing classification-based methods.

Findings

Reduces prediction error compared to individual loss functions.

Validated on CVPR Chalearn 2016 dataset with improved accuracy.

Demonstrates effectiveness of combining classification and regression losses.

Abstract

Age estimation is an essential challenge in computer vision. With the advances of convolutional neural networks, the performance of age estimation has been dramatically improved. Existing approaches usually treat age estimation as a classification problem. However, the age labels are ambiguous, thus make the classification task difficult. In this paper, we propose a simple yet effective approach for age estimation, which improves the performance compared to classification-based methods. The method combines four classification losses and one regression loss representing different class granularities together, and we name it as Age-Granularity-Net. We validate the Age-Granularity-Net framework on the CVPR Chalearn 2016 dataset, and extensive experiments show that the proposed approach can reduce the prediction error compared to any individual loss. The source code link is https://github.com/yipersevere/age-estimation.