A New Hybrid Model to Predict Human Age Estimation from Face Images Based on Supervised Machine Learning Algorithms-Reference-Cited by-同舟云学术

A New Hybrid Model to Predict Human Age Estimation from Face Images Based on Supervised Machine Learning Algorithms

Published:2023-06-01 Issue:2 Volume:23 Page:20-33
ISSN:1314-4081
Container-title:Cybernetics and Information Technologies
language:en
Short-container-title:

Author:

Al-Dujaili Mohammed Jawad¹,Ahily Hydr jabar sabat²

Affiliation:

1. 1 Department of Electronic and Communication, Faculty of Engineering , University of Kufa , Iraq

2. 2 Research & Development Department , Ministry of Higher Education & Scientific Research , Iraq

Abstract

Abstract Age estimation from face images is one of the significant topics in the field of machine vision, which is of great interest to controlling age access and targeted marketing. In this article, there are two main stages for human age estimation; the first stage consists of extracting features from the face areas by using Pseudo Zernike Moments (PZM), Active Appearance Model (AAM), and Bio-Inspired Features (BIF). In the second step, Support Vector Machine (SVM) and Support Vector Regression (SVR) algorithms are used to predict the age range of face images. The proposed method has been assessed utilizing the renowned databases of IMDB-WIKI and WIT-DB. In general, from all results obtained in the experiments, we have concluded that the proposed method can be chosen as the best method for Age estimation from face images.

Publisher

Walter de Gruyter GmbH

Subject

General Computer Science

Link

https://www.sciendo.com/pdf/10.2478/cait-2023-0011

Reference36 articles.

1. Xie, J.-C., C.-M. Pun. Deep and Ordinal Ensemble Learning for Human Age Estimation from Facial Images. – IEEE Transactions on Information Forensics and Security, Vol. 15, 2020, pp. 2361-2374.

2. Cao, W., V. Mirjalili, S. Raschka. Rank Consistent Ordinal Regression for Neural Networks with Application to Age Estimation. – Pattern Recognition Letters, Vol. 140, 2020, pp. 325-331.

3. Liu, X., et al. Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network. – Symmetry, Vol. 12, 2020, No 1, 146.

4. Bekhouche, S. E., et al. A Comparative Study of Human Facial Age Estimation: Handcrafted Features vs. Deep Features. – Multimedia Tools and Applications, Vol. 79, 2020, No 35, pp. 26605-26622.

5. Zhu, H., et al. Ordinal Distribution Regression for Gait-Based Age Estimation. – Science China Information Sciences, Vol. 63, 2020, No 2, pp. 1-14.