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Automatic wrinkle detection using hybrid hessian filter

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journal contribution
posted on 08.02.2016, 15:02 authored by Choon-Ching Ng, Moi Hoon Yap, Nicholas Costen, Baihua Li
Aging as a natural phenomenon affects different parts of the human body under the influence of various biological and environmental factors. The most pronounced changes that occur on the face is the appearance of wrinkles, which are the focus of this research. Accurate wrinkle detection is an important task in face analysis. Some have been proposed in the literature, but the poor localization limits the performance of wrinkle detection. It will lead to false wrinkle detection and consequently affect the processes such as age estimation and clinician score assessment. Therefore, we propose a hybrid Hessian filter (HHF) to cope with the identified problem. HHF is composed of the directional gradient and Hessian matrix. The proposed filter is conceptually simple, however, it significantly increases the true wrinkle localization when compared with the conventional methods. In the experimental setup, three coders have been instructed to annotate the wrinkle of 2D forehead image manually. The inter-reliability among three coders is 93 % of Jaccard similarity index (JSI). In comparison to the state-of-the-art Cula method (CLM) and Frangi filter, HHF yielded the best result with a mean JSI of 75.67 %. We noticed that the proposed method is capable of detecting the medium to coarse wrinkle but not the fine wrinkle. Although there is a gap between human annotation and automated detection, this work demonstrates that HHF is a remarkably strong filter for wrinkle detection. From the experimental results, we believe that our findings are notable in terms of the JSI.

History

School

  • Science

Department

  • Computer Science

Published in

Lecture Notes in Computer Science, LNCS-ACCV'14

Volume

9005

Pages

609 - 622 (14)

Citation

NG, C.-C. ... et al, 2015. Automatic wrinkle detection using hybrid hessian filter. IN: Cremers, D. ... et al (eds). 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part III. Lecture Notes in Computer Science; 9005, pp.609-622

Publisher

© Springer International Publishing, Switzerland

Version

AM (Accepted Manuscript)

Publisher statement

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: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2015

Notes

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-16811-1_40

ISSN

0302-9743

Book series

Lecture Notes in Computer Science;9005

Language

en

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