Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks

Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations, and occlusions. Recent studies show that deep learning approaches can achieve impressive performance on these two tasks. In this letter, we propose a deep cascaded multitask framework that exploits the inherent correlation between detection and alignment to boost up their performance. In particular, our framework leverages a cascaded architecture with three stages of carefully designed deep convolutional networks to predict face and landmark location in a coarse-to-fine manner. In addition, we propose a new online hard sample mining strategy that further improves the performance in practice. Our…

• CA
  Chinese Academy of Sciences

  Awards: 172644KYSB20160033, 172644KYSB20150019
• NS
  Natural Science Foundation of Guangdong Province

  Award: 2014A030313688