Relative Age Estimation Using Face Images

TL;DR

This paper presents a deep-learning method for more accurate age estimation from face images by refining initial estimates through reference databases and iterative modeling, achieving state-of-the-art results.

Contribution

It introduces a differential regression approach and an age augmentation scheme for improved age estimation accuracy over existing methods.

Findings

Achieves state-of-the-art accuracy on MORPH II and CACD datasets.

Effectively models age-dependent facial variations.

Reduces bias in age estimation techniques.

Abstract

This work introduces a novel deep-learning approach for estimating age from a single facial image by refining an initial age estimate. The refinement leverages a reference face database of individuals with similar ages and appearances. We employ a network that estimates age differences between an input image and reference images with known ages, thus refining the initial estimate. Our method explicitly models age-dependent facial variations using differential regression, yielding improved accuracy compared to conventional absolute age estimation. Additionally, we introduce an age augmentation scheme that iteratively refines initial age estimates by modeling their error distribution during training. This iterative approach further enhances the initial estimates. Our approach surpasses existing methods, achieving state-of-the-art accuracy on the MORPH II and CACD datasets. Furthermore, we examine the biases inherent in contemporary state-of-the-art age estimation techniques.