Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild
Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild
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DOI：
10.1109/cvprw.2017.262
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发表时间：
2017-05
期刊：
影响因子：
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通讯作者：
Zhenhua Feng;J. Kittler;Muhammad Awais;P. Huber;Xiaojun Wu
中科院分区：
文献类型：
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作者：
Zhenhua Feng;J. Kittler;Muhammad Awais;P. Huber;Xiaojun Wu
我们提出了一个框架，用于鲁棒的人脸检测和野外人脸的地标定位，该框架已被评估为“第二届面部地标定位竞赛”的一部分。该框架分为四个阶段：人脸检测、边界框聚合、姿态估计和地标定位。为了达到较高的检测率，我们使用了两个公开可用的基于cnn的人脸检测器和两个专有检测器。我们对每个输入图像的检测到的人脸边界框进行聚合，以减少误报，提高人脸检测的准确性。使用具有各种姿态变化的人脸训练的级联形状回归器，然后用于姿态估计和图像预处理。最后，我们使用大量具有有限姿态变化的训练样本，训练最终的级联形状回归器，用于细粒度地标定位。在300W和Menpo基准上获得的实验结果表明，我们的框架优于最先进的方法。
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.