.png){kind=icon}
返回
Facial Age Estimation Models for Embedded Systems: A Comparative Study
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3244059.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Age estimation
computer vision
deep learning
face detection
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks从没有面部标志的单个图像对真实年龄和表观年龄的深刻期望
PREAI
分享收藏分享收藏
查看更多内容