Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
This work addresses the problem of robust face analysis in varied conditions for computer vision applications, representing a strong incremental improvement over existing methods.
The paper tackles joint face detection and alignment in unconstrained environments by proposing a deep cascaded multi-task framework, achieving superior accuracy on benchmarks like FDDB, WIDER FACE, and AFLW while maintaining real-time performance.
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 paper, we propose a deep cascaded multi-task framework which exploits the inherent correlation between them to boost up their performance. In particular, our framework adopts a cascaded structure with three stages of carefully designed deep convolutional networks that predict face and landmark location in a coarse-to-fine manner. In addition, in the learning process, we propose a new online hard sample mining strategy that can improve the performance automatically without manual sample selection. Our method achieves superior accuracy over the state-of-the-art techniques on the challenging FDDB and WIDER FACE benchmark for face detection, and AFLW benchmark for face alignment, while keeps real time performance.