Aggregate channel features for multi-view face detection
Aggregate channel features for multi-view face detection
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DOI：
10.1109/btas.2014.6996284
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Binh Yang;Junjie Yan;Zhen Lei;S. Li
中科院分区：
文献类型：
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作者：
Binh Yang;Junjie Yan;Zhen Lei;S. Li
自从Viola和Jones的开创性工作以来，人脸检测在近几十年来引起了人们的广泛关注。虽然许多学者已经用更强大的学习算法改进了工作，但用于人脸检测的特征表示仍然不能满足有效和高效地处理具有大外观变化的人脸的需求。为了解决这个瓶颈，我们借用通道特征的概念到人脸检测领域，它将图像通道扩展到不同的类型，如梯度幅度和定向梯度直方图，从而以简单的形式编码丰富的信息。我们采用了一种新的变种称为聚合通道功能，进行了充分的探索功能设计，并发现了一个多尺度版本的功能具有更好的性能。针对野外人脸姿态检测问题，提出了一种基于分数重排序和检测调整的多视角检测方法。遵循Viola-Jones框架中的学习管道，使用聚合通道特征的多视图人脸检测器在AFW和FDDB测试集上显示出与最先进算法竞争的性能，同时在VGA图像上以42 FPS运行。
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 powerful learning algorithms, the feature representation used for face detection still can't meet the demand for effectively and efficiently handling faces with large appearance variance 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 gradient magnitude and oriented gradient histograms and therefore 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 detection adjustment. Following the learning pipelines in Viola-Jones framework, the multi-view face detector using aggregate 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.