A Unified Gender-Aware Age Estimation

TL;DR

This paper introduces a unified gender-aware age estimation framework that considers gender differences and semantic relationships, improving accuracy and interpretability over previous methods that either classify then estimate or concatenate labels.

Contribution

It proposes a novel unified approach that models gender and age jointly, capturing their semantic relationship and aging differences, addressing limitations of prior sequential and concatenation methods.

Findings

Outperforms existing methods in age estimation accuracy.

Effectively models gender and age relationship and differences.

Provides interpretable insights into aging discrepancies.

Abstract

Human age estimation has attracted increasing researches due to its wide applicability in such as security monitoring and advertisement recommendation. Although a variety of methods have been proposed, most of them focus only on the age-specific facial appearance. However, biological researches have shown that not only gender but also the aging difference between the male and the female inevitably affect the age estimation. To our knowledge, so far there have been two methods that have concerned the gender factor. The first is a sequential method which first classifies the gender and then performs age estimation respectively for classified male and female. Although it promotes age estimation performance because of its consideration on the gender semantic difference, an accumulation risk of estimation errors is unavoidable. To overcome drawbacks of the sequential strategy, the second is to regress the age appended with the gender by concatenating their labels as two dimensional output using Partial Least Squares (PLS). Although leading to promotion of age estimation performance, such a concatenation not only likely confuses the semantics between the gender and age, but also ignores the aging discrepancy between the male and the female. In order to overcome their shortcomings, in this paper we propose a unified framework to perform gender-aware age estimation. The proposed method considers and utilizes not only the semantic relationship between the gender and the age, but also the aging discrepancy between the male and the female. Finally, experimental results demonstrate not only the superiority of our method in performance, but also its good interpretability in revealing the aging discrepancy.