Active appearance model algorithm with k-nearest neighbor classifier for face pose estimation

Bing-Fei Wu*, Chih Chung Kao, Cheng Lung Jen, Chia Rong Chiang, Po Hung Lai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In this paper, a face pose estimation (FPE) algorithm using active appearance model (AAM) with a k-nearest neighbor (kNN) classifier is presented. AAM is a model-based image representation method used to describe non-rigid visual objects, with both shape and texture variations, using a mean vector and linear combinations of a set of variation modes. Since AAM is a deformable model, it has several variations. Owing to the variations, the model is adjusted to the input test face image using iterative searching and fitting. The error, which measures the difference between the model and a test image, is minimized with the proposed searching algorithm. The face pose is then estimated using the distances between the landmark points in the AAM model with a kNN classifier. Experimental results demonstrate that the proposed FPE algorithm can fit the face location with different face poses, with or without a hat, even wearing glasses, and identify the face pose accurately.

Original languageEnglish
Pages (from-to)285-294
Number of pages10
JournalJournal of Marine Science and Technology (Taiwan)
Volume22
Issue number3
DOIs
StatePublished - 1 Jan 2014

Keywords

  • Active appearance model
  • Face pose estimation
  • k-nearest neighbor
  • Shape model
  • Texture model

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