Robust real-time face detection
Robust real-time face detection
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DOI：
10.1023/b:visi.0000013087.49260.fb
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发表时间：
2004-05-01
影响因子：
19.5
通讯作者：
Jones, MJ
中科院分区：
文献类型：
--
作者：
Viola, P;Jones, MJ
本文描述了一种人脸检测框架，它能够以极快的速度处理图像，同时实现高检测率。主要贡献有三个。首先是引入了一种新的图像表示法，称为“积分图像”，它允许我们的检测器使用的特征非常快速地计算出来。第二种是简单而有效的分类器，它使用AdaBoost学习算法(Freund和Schaplire，1995)来从非常大的潜在特征集中选择少量关键视觉特征。第三个贡献是一种将分类器组合成级联的方法，该方法允许快速丢弃图像的背景区域，同时在有希望的人脸区域上花费更多的计算。在人脸检测领域进行了一系列实验。该系统产生的人脸检测性能可与以前最好的系统相媲美(Sung和Poggio，1998；Rowley等人，1998；Schneiderman和Kanade，2000；Roth等人，2000)。在传统桌面上实现的人脸检测以每秒15帧的速度进行。
This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the "Integral Image" which allows the features used by our detector to be computed very quickly. The second is a simple and efficient classifier which is built using the AdaBoost learning algorithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of potential features. The third contribution is a method for combining classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. A set of experiments in the domain of face detection is presented. The system yields face detection performance comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman and Kanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, face detection proceeds at 15 frames per second.