Multi-task GLOH feature selection for human age estimation

TL;DR

This paper introduces a multi-task learning approach using GLOH features for human age estimation, effectively reducing feature dimensionality and computational load while maintaining high accuracy.

Contribution

It presents a novel combination of GLOH feature descriptor with multi-task learning for efficient and accurate age estimation from face images.

Findings

Achieves comparable accuracy with fewer features.

Reduces computational complexity significantly.

Demonstrates effectiveness on FG-NET database.

Abstract

In this paper, we propose a novel age estimation method based on GLOH feature descriptor and multi-task learning (MTL). The GLOH feature descriptor, one of the state-of-the-art feature descriptor, is used to capture the age-related local and spatial information of face image. As the exacted GLOH features are often redundant, MTL is designed to select the most informative feature bins for age estimation problem, while the corresponding weights are determined by ridge regression. This approach largely reduces the dimensions of feature, which can not only improve performance but also decrease the computational burden. Experiments on the public available FG-NET database show that the proposed method can achieve comparable performance over previous approaches while using much fewer features.