Handling missing weak classifiers in boosted cascade: application to multiview and occluded face detection

P Bouges, T Chateau, C Blanc, G Loosli
EURASIP Journal on Image and Video Processing 2013 (1), 1-22

publication in Eurasip, 2013
publication in Eurasip, 2013


We propose a generic framework to handle missing weak classifiers at testing stage in a boosted cascade. The main contribution is a probabilistic formulation of the cascade structure that considers the uncertainty introduced by missing weak classifiers. This new formulation involves two problems: (1) the approximation of posterior probabilities on each level and (2) the computation of thresholds on these probabilities to make a decision. Both problems are studied, and several solutions are proposed and evaluated. The method is then applied to two popular computer vision applications: detecting occluded faces and detecting faces in a pose different than the one learned. Experimental results are provided using conventional databases to evaluate the proposed strategies related to basic ones.


  • Pattern recognition
  • Supervised learning
  • Object detection
  • Missing data
  • Adaptation
  • Face