Face Alignment Based on Statistical Models Using SIFT Descriptors

Zisheng LI  Jun-ichi IMAI  Masahide KANEKO  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E92-A    No.12    pp.3336-3343
Publication Date: 2009/12/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E92.A.3336
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Image Media Quality)
Category: Processing
face alignment,  ASM,  SIFT,  GentleBoost,  

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Active Shape Model (ASM) is a powerful statistical tool for image interpretation, especially in face alignment. In the standard ASM, local appearances are described by intensity profiles, and the model parameter estimation is based on the assumption that the profiles follow a Gaussian distribution. It suffers from variations of poses, illumination, expressions and obstacles. In this paper, an improved ASM framework, GentleBoost based SIFT-ASM is proposed. Local appearances of landmarks are originally represented by SIFT (Scale-Invariant Feature Transform) descriptors, which are gradient orientation histograms based representations of image neighborhood. They can provide more robust and accurate guidance for search than grey-level profiles. Moreover, GentleBoost classifiers are applied to model and search the SIFT features instead of the unnecessary assumption of Gaussian distribution. Experimental results show that SIFT-ASM significantly outperforms the original ASM in aligning and localizing facial features.