Noise-Tolerance GPU-based Age Estimation Using ResNet-50

TL;DR

This paper presents a GPU-accelerated ResNet-50 based age estimation system that significantly improves accuracy and demonstrates strong noise tolerance, making it robust for real-world applications.

Contribution

The work introduces a deep residual neural network for age estimation that outperforms existing methods and maintains high accuracy under noisy conditions.

Findings

28.3% improvement in MAE over recent methods

71.39% MAE reduction compared to AlexNet

Less than 1.5% performance degradation with 15 dB noise

Abstract

The human face contains important and understandable information such as personal identity, gender, age, and ethnicity. In recent years, a person's age has been studied as one of the important features of the face. The age estimation system consists of a combination of two modules, the presentation of the face image and the extraction of age characteristics, and then the detection of the exact age or age group based on these characteristics. So far, various algorithms have been presented for age estimation, each of which has advantages and disadvantages. In this work, we implemented a deep residual neural network on the UTKFace data set. We validated our implementation by comparing it with the state-of-the-art implementations of different age estimation algorithms and the results show 28.3% improvement in MAE as one of the critical error validation metrics compared to the recent works and also 71.39% MAE improvements compared to the implemented AlexNet. In the end, we show that the performance degradation of our implemented network is lower than 1.5% when injecting 15 dB noise to the input data (5 times more than the normal environmental noise) which justifies the noise tolerance of our proposed method.