Robust Face Detection from Still Images

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Authors
  1. Laytner, P.
  2. Ling, C.
  3. Xiao, Q.
Corporate Authors
Defence Research and Development Canada, Ottawa Research Centre, Ottawa ON (CAN);Waterloo Univ, Waterloo ONT (CAN)
Abstract
Facial recognition is one of the most studied topics in the field of biometrics because of its varied applications. Detection of dark colored faces and poorly illuminated faces are not well studied in the literature due to several challenges. The most critical challenge is that there is inadequate contrast among facial features. To overcome this challenge, a new face detection methodology, which consists of histogram analysis, Haar wavelet transformation and Adaboost learning techniques, is proposed. The extended Yale Face Database B is used to examine the performance of the proposed method and compared against commonly used OpenCV’s Haar detection algorithm. The experimental results with 9,883 positive images and 10,349 negative images showed a considerable improvement in face hit rates without a significant change in false acceptance rates.
Keywords
face detection;illumination;skin color variation;Haar-like features;OpenCV
Report Number
DRDC-RDDC-2014-P47 — External Literature
Date of publication
01 Jan 2014
Number of Pages
5
DSTKIM No
CA039592
CANDIS No
800532
Format(s):
Electronic Document(PDF)

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