Autor(en)
Zhang, Kaipeng; Zhang, Zhanpeng; Li, Zhifeng; Qiao, Yu
Titel
Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
Teil von
  • IEEE signal processing letters, 2016-10, Vol.23 (10), p.1499-1503
Ort / Verlag
PISCATAWAY: IEEE
Links zum Volltext
Quelle
IEEE Electronic Library (IEL) Journals
Beschreibungen
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.
Format
Sprache(n)
Englisch
Identifikator(en)
ISSN: 1070-9908
ISSN: 1558-2361
DOI: 10.1109/LSP.2016.2603342

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