文件名称：Aggregate Channel Features for Multi-view Face Detection
文件大小：5.08MB
文件格式：PDF
更新时间：2018-10-16 17:00:21
Face Detection
Face detection has drawn much attention in recent decades since the seminal work by Viola and Jones. While many subsequences have improved the work with more pow- erful learning algorithms, the feature representation used for face detection still can’t meet the demand for effectively and efficiently handling faces with large appearance vari- ance in the wild. To solve this bottleneck, we borrow the concept of channel features to the face detection domain, which extends the image channel to diverse types like gradi- ent magnitude and oriented gradient histograms and there- fore encodes rich information in a simple form. We adopt a novel variant called aggregate channel features, make a full exploration of feature design, and discover a multi- scale version of features with better performance. To deal with poses of faces in the wild, we propose a multi-view detection approach featuring score re-ranking and detec- tion adjustment. Following the learning pipelines in Viola- Jones framework, the multi-view face detector using ag- gregate channel features shows competitive performance against state-of-the-art algorithms on AFW and FDDB test- sets, while runs at 42 FPS on VGA images.