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Information Technology Journal
  Year: 2011 | Volume: 10 | Issue: 8 | Page No.: 1518-1526
DOI: 10.3923/itj.2011.1518.1526
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Entropy-Directed AdaBoost Algorithm with NBBP Features for Face Detection

Hsun-Li Chang and Tai-Wen Yue

AdaBoost learning algorithm had achieved good performance for real-time face detection with Haar-like features. Although the great achievement had been reached by AdaBoost, the learning phase is really time-consuming. This study introduces the so-called Neighboring-Block Binary Pattern (NBBP) features and associated each of them with a feature entropy to improve learning efficiency. By computing the entropies of NBBP features, the best weak-classifier of each iteration can be determined systematically in a non-brute-force manner. The concept is applied to build a real-time face detection system. Comparisons with other approaches will be presented in the study, including Receiver Operating Characteristic (ROC) and training efficiency. For still images, experimental results showed that the NBBP features are intrinsically superior to Haar-like ones in many perspectives, e.g., the former is illumination invariant and more efficacious in discriminate power. For 320x240 video sequences, we achieved the performance about 22 fps. In particular, the proposed feature selection methodology based on entropy provides a precise criterion to measure the discrimination ability of available features and, hence, can serve as a ‘referee’ for AdaBoost algorithm to construct weak classifiers effectively.
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How to cite this article:

Hsun-Li Chang and Tai-Wen Yue, 2011. Entropy-Directed AdaBoost Algorithm with NBBP Features for Face Detection. Information Technology Journal, 10: 1518-1526.

DOI: 10.3923/itj.2011.1518.1526






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