LI-008 Feature Selection By AdaBoost For SVM-Based Face Detection

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概要

• 論文の詳細を見る

• In this paper, we present a three-stage method to speed up a SVM-based face detection system. In this proposed system, a large number of simple non-face patterns are rejected quickly by two first stage cascaded classifiers using flexible sizes of analyzed windows while the last stage uses a non linear SVM classifier to robustly classify complex 24x24 pixel patterns as either faces or non-faces. For all stage classifiers, an optimal subset of overcomplete Haar wavelet feature set selected by AdaBoost learning is used to achieve both fast and high detection rate. Experimental results show that our system can achieve comparable results to state of the art face detection systems.

• FIT(電子情報通信学会・情報処理学会)推進委員会の論文
• 2004-08-20

著者

• Satoh Shin'ichi

  The Graduate University For Advanced Studies:national Institute Of Informatics

• LE Duy

  The Graduate University for Advanced Studies

関連論文

• LI-008 Feature Selection By AdaBoost For SVM-Based Face Detection
• An Efficient Feature Selection Method For Object Detection
• An Efficient Feature Selection Method For Object Detection

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