An adaptive-profile active shape model for facial-feature detection

Ke Sun, Huiling Zhou, Kin Man Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

In this paper, a novel algorithm based on the Active Shape Model (ASM) for locating landmarks on human faces is proposed. A challenge for detecting facial features is that faces may be under different poses, this makes the local appearance of each facial landmark vary greatly. To account for these variations, we propose an adaptive-profile scheme for ASM so that facial landmarks can be detected reliably and accurately under different poses. In our algorithm, a 2D profile is used for each landmark, and the 2D profiles of each landmark of the training face images are grouped to form a number of clusters. The corresponding shape vector for each of the clusters is then learned. For a query face image, the profiles to be used to locate the respective facial landmarks will be selected according to the face-shape vector in the current iteration. In other words, adaptive profiles are used in the search for landmarks. Face images from two subsets of the IMM Face Database are used for training, and the other two subsets are used for testing. The performance of our proposed algorithm is also evaluated using another dataset, namely the Bosphorus Dataset. Experiment results show that our proposed Adaptive-Profile Active Shape Model (APASM) can locate facial landmarks accurately under different face shapes, expressions, and poses.
Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherIEEE
Pages2849-2854
Number of pages6
ISBN (Electronic)9781479952083
DOIs
Publication statusPublished - 1 Jan 2014
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014

Conference

Conference22nd International Conference on Pattern Recognition, ICPR 2014
CountrySweden
CityStockholm
Period24/08/1428/08/14

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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