Naslov
Facial Age Estimation Models for Embedded Systems: A Comparative Study

Autori
Doždor, Zorana ; Hrkać, Tomislav ; Brkić, Karla ; Kalafatić, Zoran

Izvornik
IEEE Access (2169-3536) 11 (2023); 14282-14292

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
age estimation ; computer vision ; deep learning ; face detection

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA