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Proceedings Paper

Robust face detection using individual face parts classifiers based on AdaBoost
Author(s): Kiyoto Ichikawa; Takeshi Mita; Osamu Hori
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Paper Abstract

We present a robust frontal face detection method that enables the identification of face positions in images by combining the results of a low-resolution whole face and individual face parts classifiers. Our approach is to use face parts information and change the identification strategy based on the results from individual face parts classifiers. Faces are detected by scanning the classifiers into an input image. The classifiers for whole face and individual face parts detection were implemented based on an AdaBoost algorithm. We propose a novel method based on a decision tree to improve performance of face detectors for occluded faces. The proposed decision tree method distinguishes partially occluded faces based on the results from the individual classifies. Preliminarily experiments on a test sample set containing non-occluded faces and occluded faces indicated that our method achieved better results than conventional methods. Actual experimental results containing real images also showed better results.

Paper Details

Date Published: 6 December 2005
PDF: 12 pages
Proc. SPIE 6051, Optomechatronic Machine Vision, 60510D (6 December 2005); doi: 10.1117/12.648701
Show Author Affiliations
Kiyoto Ichikawa, Tokyo Institute of Technology (Japan)
Takeshi Mita, Toshiba Corp. (Japan)
Osamu Hori, Toshiba Corp. (Japan)
Tokyo Institute of Technology (Japan)


Published in SPIE Proceedings Vol. 6051:
Optomechatronic Machine Vision

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