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Dual-threshold Adaboost Face Detection Algorithm Based on Feature Pruning

  

  1. (1. College of Computer and Information, Hohai University, Nanjing 211100, China;

    2. College of Electrical Engineering and Automation, Sanjiang University, Nanjing 210012, China)
  • Received:2014-05-07 Online:2014-08-15 Published:2014-08-19

Abstract:

Aiming at the problem of too much training time by using traditional Adaboost, this paper proposes a
novel dual-Adaboost face deteetion algorithm based feature pruning. On the one hand, the usage of dual-threshold
weak classifiers which replaced the traditional single-threshold weak classifier improves the classification
capability on individual weak classifier. On the other hand, the algorithm uses only the samples with small error
rate to train the weak classifier. Experimental results show that the training speed is increased by using less
features and a small proportion of the features in this dual-Adaboost algorithm.

Key words: face detection, Adaboost, feature pruning, dual-threshold

CLC Number: