Joint Cascade Face Detection and Alignment

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8694)


We present a new state-of-the-art approach for face detection. The key idea is to combine face alignment with detection, observing that aligned face shapes provide better features for face classification. To make this combination more effective, our approach learns the two tasks jointly in the same cascade framework, by exploiting recent advances in face alignment. Such joint learning greatly enhances the capability of cascade detection and still retains its realtime performance. Extensive experiments show that our approach achieves the best accuracy on challenging datasets, where all existing solutions are either inaccurate or too slow.


Face Detection Face Shape Sift Descriptor Split Test Facial Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.University of Science and Technology of ChinaChina
  2. 2.Microsoft ResearchUSA

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