Jadid, MA and Rezaei, M orcid.org/0000-0003-3892-421X (2017) Facial age estimation using hybrid Haar wavelet and color features with Support Vector Regression. In: 2017 Artificial Intelligence and Robotics (IRANOPEN). 2017 Artificial Intelligence and Robotics (IranOpen) : the 7th Conference on Artificial Intelligence and Robotics, 09 Apr 2017, Islamic Azad University, Iran. IEEE , pp. 6-12. ISBN 978-1-5386-2862-1
Abstract
Face appearance is one of the most important visual features of human which varies significantly over the aging. Therefore, automatic age estimation is a demanding research topic in the field of facial feature analysis. In the task of age estimation, feature extraction is the first influential step which highly effects on a learning method and its obtained results. The second important step of an age estimation system is training of pattern recognition method based on the extracted feature vector. Considering the importance of the feature extraction and training steps, this paper utilizes the combination of Haar wavelet transform and color moment approaches to extract full-informative and influencing feature elements of face image. To improve the training step, the paper trains a Support Vector Regression (SVR) model, based on the extracted feature vector for age estimation. Experimental results of the proposed method are performed on FG-NET and MORPH datasets and prove the superiority of the method compared with the state-of-the-art methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | age estimationt; Haar wavelet; Support Vector Regression. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Feb 2020 13:39 |
Last Modified: | 21 Feb 2020 13:48 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/rios.2017.7956436 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157460 |