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An investigation on local wrinkle-based extractor of age estimation

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conference contribution
posted on 09.02.2016, 14:58 by Choon-Ching Ng, Moi Hoon Yap, Nicholas Costen, Baihua Li
Research related to age estimation using face images has become increasingly important due to its potential use in various applications such as age group estimation in advertising and age estimation in access control. In contrast to other facial variations, age variation has several unique characteristics which make it a challenging task. As we age, the most pronounced facial changes are the appearance of wrinkles (skin creases), which is the focus of ageing research in cosmetic and nutrition studies. This paper investigates an algorithm for wrinkle detection and the use of wrinkle data as an age predictor. A novel method in detecting and classifying facial age groups based on a local wrinkle-based extractor (LOWEX) is introduced. First, each face image is divided into several convex regions representing wrinkle distribution areas. Secondly, these areas are analysed using a Canny filter and then concatenated into an enhanced feature vector. Finally, the face is classified into an age group using a supervised learning algorithm. The experimental results show that the accuracy of the proposed method is 80% when using FG-NET dataset. This investigation shows that local wrinkle-based features have great potential in age estimation. We conclude that wrinkles can produce a prominent ageing descriptor and identify some future research challenges.



  • Science


  • Computer Science

Published in

VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications




675 - 681


NG, C.-C. ... et al, 2014. An investigation on local wrinkle-based extractor of age estimation. VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications, Lisbon, Portugal, 5th-8th January 2014, pp.675-681


IEEE and INSTICC (© 2014 SCITEPRESS - Science and Technology Publications)


AM (Accepted Manuscript)

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This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at:

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