We present a framework for robust face detection and landmark localisation of
faces in the wild, which has been evaluated as part of `the 2nd Facial Landmark
Localisation Competition'. The framework has four stages: face detection,
bounding box aggregation, pose estimation and landmark localisation. To achieve
a high detection rate, we use two publicly available CNN-based face detectors
and two proprietary detectors. We aggregate the detected face bounding boxes of
each input image to reduce false positives and improve face detection accuracy.
A cascaded shape regressor, trained using faces with a variety of pose
variations, is then employed for pose estimation and image pre-processing.
Last, we train the final cascaded shape regressor for fine-grained landmark
localisation, using a large number of training samples with limited pose
variations. The experimental results obtained on the 300W and Menpo benchmarks
demonstrate the superiority of our framework over state-of-the-art methods.

提出一种框架，包括四个阶段：脸部检测，边界框聚合，姿态估计和特征点定位。实验结果表明，该框架在人脸检测和特征点定位方面优于其他先进算法。