A Demographic Attribute Guided Approach to Age Estimation

TL;DR

This paper introduces a novel age estimation method that leverages demographic attributes and a multi-scale attention residual convolution unit to improve accuracy, demonstrating superior results on multiple datasets.

Contribution

The paper proposes a new demographic attribute-guided approach with a specialized feature extraction module and a novel loss function for more accurate age estimation.

Findings

Achieves superior performance on UTKFace, LAP2016, and Morph datasets.

Outperforms state-of-the-art methods in age estimation accuracy.

Introduces a new error compression ranking loss for better convergence.

Abstract

Face-based age estimation has attracted enormous attention due to wide applications to public security surveillance, human-computer interaction, etc. With vigorous development of deep learning, age estimation based on deep neural network has become the mainstream practice. However, seeking a more suitable problem paradigm for age change characteristics, designing the corresponding loss function and designing a more effective feature extraction module still needs to be studied. What is more, change of face age is also related to demographic attributes such as ethnicity and gender, and the dynamics of different age groups is also quite different. This problem has so far not been paid enough attention to. How to use demographic attribute information to improve the performance of age estimation remains to be further explored. In light of these issues, this research makes full use of auxiliary information of face attributes and proposes a new age estimation approach with an attribute guidance module. We first design a multi-scale attention residual convolution unit (MARCU) to extract robust facial features other than simply using other standard feature modules such as VGG and ResNet. Then, after being especially treated through full connection (FC) layers, the facial demographic attributes are weight-summed by 1*1 convolutional layer and eventually merged with the age features by a global FC layer. Lastly, we propose a new error compression ranking (ECR) loss to better converge the age regression value. Experimental results on three public datasets of UTKFace, LAP2016 and Morph show that our proposed approach achieves superior performance compared to other state-of-the-art methods.