2.5D face landmarking via scale-invariant feature extraction and centroid localization

Suk, Ting Pui and Terrin, Lim and Jacey-Lynn, Minoi (2016) 2.5D face landmarking via scale-invariant feature extraction and centroid localization. 3rd International Conference on Computer and Information Sciences (ICCOINS), 2016. ISSN ISBN : 978-1-5090-2549-7

2.5D Face Landmarking via Scale-invariant Feature (abstract).pdf

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Official URL: http://ieeexplore.ieee.org/document/7783244/


In this paper, we present and discuss our proposed method on landmarking on 2.5-dimensional (2.5D) face range images. Face landmarking plays an important role as an intermediary component in several face processing operation applications. Locating facial landmarks automatically remains a challenge. Detecting and localizing landmarks from raw face data are often performed manually by trained and experienced scientists or clinicians, and the process is usually lengthy, laborious and tedious. In order to overcome these challenges, we introduce a method that employs geometric approach, through utilizing the mean and Gaussian curvatures, primitive surfaces information to identify and label features as anatomical landmarks. In addition, comparative experiments on both automatic landmarking and manual landmarking were also performed and the results have demonstrated that the proposed method outperforms the manual landmarking in terms of obtaining distinct facial landmarks correctly and accurately.

Item Type: Article
Uncontrolled Keywords: mean and Gaussian curvature, feature extraction, centroid localization, geometric approach, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: T Technology > T Technology (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Karen Kornalius
Date Deposited: 08 Jun 2017 06:02
Last Modified: 08 Jun 2017 06:02
URI: http://ir.unimas.my/id/eprint/16543

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