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Clip: 1 Abstract—Face detection and alignment in unconstrained en- vironment 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 paper, we propose a deep cascaded multi-task framework which 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 land- mark 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 ac- curacy over the state-of-the-art techniques on the challenging FDDB and WIDER FACE benchmarks for face detection, and

Filename: spl.pdf

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