期刊
IEEE SIGNAL PROCESSING LETTERS
卷 23, 期 10, 页码 1499-1503出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2016.2603342
关键词
Cascaded convolutional neural network (CNN); face alignment; face detection
资金
- External Cooperation Program of BIC
- Chinese Academy of Sciences [172644KYSB20160033, 172644KYSB20150019]
- Shenzhen Research Program [KQCX2015033117354153, JSGG20150925164740726, CXZZ20150930104115529, CYJ20150925163005055, JCYJ201 60510154736343]
- Guangdong Research Program [2014B050505017, 2015B010129013]
- Natural Science Foundation of Guangdong Province [2014A030313688]
- Key Laboratory of Human Machine Intelligence-Synergy Systems through the Chinese Academy of Sciences
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 method achieves superior accuracy over the state-of-the-art techniques on the challenging face detection dataset and benchmark and WIDER FACE benchmarks for face detection, and annotated facial landmarks in the wild benchmark for face alignment, while keeps real-time performance.
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