CDBN: Crow Deep Belief Network Based on Scattering and AAM Features for Age Estimation-Reference-Cited by-同舟云学术

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CDBN: Crow Deep Belief Network Based on Scattering and AAM Features for Age Estimation

Published:2020-11-02 Issue:8 Volume:93 Page:879-897
ISSN:1939-8018
Container-title:Journal of Signal Processing Systems
language:en
Short-container-title:J Sign Process Syst

Author:

Shejul Anjali A.,Kinage Kishor S.,Reddy B. Eswara

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Modelling and Simulation,Information Systems,Signal Processing,Theoretical Computer Science,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11265-020-01609-z.pdf

Reference47 articles.

1. Xing, J., Li, K., Hu, W., Yuan, C., & Ling, H. (June 2017). Diagnosing deep learning models for high accuracy age estimation from a single image. Pattern Recognition, 66, 106–116.

2. Wang, S., Tao, D., & Yang, J. (2016). Relative attribute SVM+ learning for age estimation. IEEE Transactions on Cybernetics, 46(3), 827–839.

3. Hu, C., Gong, L., Wang, T., & Feng, Q. (June 2015). Effective human age estimation using a two-stage approach based on lie Algebrized Gaussians feature. Multimedia Tools and Applications, 74(11), 4139–4159.

4. Zhao, W., & Wang, H. (2015). Facial age estimation based on advanced ordinal ranking. Electronics Letters, 51(12), 903–904.

5. Guo, G. G., Mu, G. W., Fu, Y., & Huang, T. S. (2009). Human age estimation using bio-inspired features. IEEE Proceedings Conference of Computer Vision and Pattern Recognition, 112–119.

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1. Automated handcrafted features with deep learning based age group estimation model using facial profiles;Multimedia Tools and Applications;2023-10-16

2. Human face aging based on active appearance model using proper feature set;Signal, Image and Video Processing;2022-10-18

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