A Unified Gender-Aware Age Estimation
Qing Tian, Songcan Chen, and Xiaoyang Tan

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…
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Taxonomy
TopicsFace recognition and analysis · Video Surveillance and Tracking Methods · Human Pose and Action Recognition
