Facial Age Estimation Models for Embedded Systems: A Comparative Study

Automated age estimation from face images is the process of assigning either an exact age or a specific age range to a facial image. In this paper a comparative study of the current techniques suitable for this task is performed, with an emphasis on lightweight models suitable for embedded implementation. We investigate both the suitable modern deep learning architectures for feature extraction and the variants of framing the problem itself as either classification, regression or soft label classification. The models are evaluated on Audience dataset for age group classification and FG-NET dataset for exact age estimation. To gather in-depth insights into automated age estimation and in contrast to existing studies, we additionally compare the performance of both classification and regression on the same dataset. We propose a novel loss function that combines regression and classification approaches and show that it outperforms other considered approaches. At the same time, with a lightweight backbone, such an architecture is suitable for implementation on embedded devices.

Automated summary

This paper conducts a comparative study on the current techniques for automated age estimation from face images, focusing on lightweight models suitable for embedded implementation. The study investigates modern deep learning architectures for feature extraction and different ways of framing the problem as classification, regression, or soft label classification. The models are evaluated on the Audience dataset for age group classification and the FG-NET dataset for exact age estimation. The paper proposes a novel loss function that combines regression and classification approaches and demonstrates its superior performance compared to other methods. Moreover, the lightweight architecture is suitable for implementation on embedded devices.