Vol.69, No.2, 2021, pp.2203-2216, doi:10.32604/cmc.2021.018709
Face Age Estimation Based on CSLBP and Lightweight Convolutional Neural Network
  • Yang Wang1, Ying Tian1,*, Ou Tian2
1 School of Computer Science and Software Engineering, University of Science & Technology Liaoning, Anshan, 114051, China
2 Medical and Health Sciences, University of Wollongong, NSW, 2522, Australia
* Corresponding Author: Ying Tian. Email:
Received 17 March 2021; Accepted 18 April 2021; Issue published 21 July 2021
As the use of facial attributes continues to expand, research into facial age estimation is also developing. Because face images are easily affected by factors including illumination and occlusion, the age estimation of faces is a challenging process. This paper proposes a face age estimation algorithm based on lightweight convolutional neural network in view of the complexity of the environment and the limitations of device computing ability. Improving face age estimation based on Soft Stagewise Regression Network (SSR-Net) and facial images, this paper employs the Center Symmetric Local Binary Pattern (CSLBP) method to obtain the feature image and then combines the face image and the feature image as network input data. Adding feature images to the convolutional neural network can improve the accuracy as well as increase the network model robustness. The experimental results on IMDB-WIKI and MORPH 2 datasets show that the lightweight convolutional neural network method proposed in this paper reduces model complexity and increases the accuracy of face age estimations.
Face age estimation; lightweight convolutional neural network; CSLBP; SSR-Net
Cite This Article
Wang, Y., Tian, Y., Tian, O. (2021). Face Age Estimation Based on CSLBP and Lightweight Convolutional Neural Network. CMC-Computers, Materials & Continua, 69(2), 2203–2216.
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