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Aggregate Channel Features for Multi-view Face Detection 评分:

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 appeara nce 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.
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2015-09-26 上传大小:5.08MB
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