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Facial age estimation using pre-trained CNN and transfer learning
- Source :
- Multimedia Tools and Applications. 80:20369-20380
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- This paper tackled the problem of human facial age estimation using transfer learning of some pre-trained CNNs, namely VGG, Res-Net, Google-Net, and Alex-Net. Those networks have been fine-tuned with transfer learning and undergone many experiments to get the optimum number of outputs and the optimum age gap. Based on those experiments, a novel hierarchical network that generates high age estimation accuracy was developed. This new network consists of a set of pre-trained 2-classes CNNs (Google-Net) with an optimum age gap which can better organize the face images in the age group they belong to. To show its effectiveness, it was compared with other states of the art techniques on the FGNET and the MORPH databases.
- Subjects :
- Computer Networks and Communications
business.industry
Group (mathematics)
Computer science
020207 software engineering
02 engineering and technology
Machine learning
computer.software_genre
Set (abstract data type)
Hardware and Architecture
Age estimation
Face (geometry)
0202 electrical engineering, electronic engineering, information engineering
Media Technology
Artificial intelligence
business
Transfer of learning
computer
Software
Subjects
Details
- ISSN :
- 15737721 and 13807501
- Volume :
- 80
- Database :
- OpenAIRE
- Journal :
- Multimedia Tools and Applications
- Accession number :
- edsair.doi...........a103ecd5dfa2c24ec9e793fc83bbb7d0