Abstract
Human age estimation is one of the most challenging problem because it can be used in many applications relating to age such as age-specific movies, age-specific computer applications or website, etc. This paper will contribute to give brief information about development of age estimation researches using deep learning. We explore three recent journal papers that give significant contribution in age estimation using deep learning. From these papers, they selected classification methods and there is gradual improvement in result and also in selected loss function. The best result gives MAE (mean average error) 2.8 years and VGG-16 is the most selected CNN architecture.
Highlights
Human age can be estimated by facial appearance
Deep learning schemes, especially Convolutional Neural Networks (CNNs), have been successfully employed for many tasks related to facial analysis
This paper aims to provide a brief description about some papers that have done age estimation research using CNN or deep learning
Summary
Human age can be estimated by facial appearance. The photo taken at different years indicate the aging process on their faces. Estimating age from images is one of the most challenging work in facial analysis. Deep learning schemes, especially Convolutional Neural Networks (CNNs), have been successfully employed for many tasks related to facial analysis. This paper aims to provide a brief description about some papers that have done age estimation research using CNN or deep learning. We will limit discussion to only a few paper published in journals or conferences in the last 5 years and became an important milestone of age estimating work. This paper is organized as follows: in section 2, age estimation algorithm will be explained and, we will explain about CNN architecture This paper is organized as follows: in section 2, age estimation algorithm will be explained and in section 3, we will explain about CNN architecture
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Applied Sciences and Smart Technologies
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
