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Automatic Wrinkle Detection Using Hybrid Hessian Filter

  • Choon-Ching NgEmail author
  • Moi Hoon Yap
  • Nicholas Costen
  • Baihua Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9005)

Abstract

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.

Keywords

Hessian Matrix Manual Annotation Edge Detection Method Jaccard Similarity Index Facial Wrinkle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was supported by Manchester Metropolitan University’s PhD Studentship. The authors gratefully acknowledge the contribution of reviewers’ comments. They would also like to thank the Savran et al. [20] for providing the Bosphorus dataset.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Choon-Ching Ng
    • 1
    Email author
  • Moi Hoon Yap
    • 1
  • Nicholas Costen
    • 1
  • Baihua Li
    • 1
  1. 1.School of Computing, Mathematics & Digital TechnologyManchester Metropolitan UniversityManchesterUK

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