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Joint Cascade Face Detection and Alignment
Dong Chen1, Shaoqing Ren1, Yichen Wei2, Xudong Cao2, and Jian Sun2
1University of Science and Technology of China, China
chendong@mail.ustc.edu.cn
sqren@mail.ustc.edu.cn
2Microsoft Research, USA
yichenw@microsoft.com
xudongca@microsoft.com
jiansun@microsoft.com
Abstract. 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.
LNCS 8694, p. 109 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014