Understanding Image-feature and Decision-procedure Choice for Human Face Detection

理解图像特征和决策程序选择人脸检测NSF Proposal 0413284 AbstractFace检测算法在过去五年中有了显著的改进，部分原因是从手工制作的系统转向监督学习系统，从大量的训练数据中发现有效的特征组合。这个广泛的表征涵盖了三个突出的算法：Viola和Jones Ada Boost算法，Schneiderman的统计模型方法和Yang等人的SNoW算法。然而，除了这个广泛的表征，这些算法在细节上有很大的不同。特别是，它们使用不同的图像特征和不同的决策程序来确定面部是否存在。为什么这些非常不同的算法都表现得相对较好，这些算法中隐含的图像特征和决策过程选择是如何相互作用的？这项研究将揭示这三种方法和其他方法的基本原理，建立一个共同的框架，结合和比较图像特征提取和决策程序。经验上的进步将在表征人脸检测任务的算法独立的方式，更好的人脸检测算法的发展至关重要。 这项研究的实际影响是双重的。三个主要人脸检测算法的新开源版本将被开发并集成到当前的CSU人脸识别评估系统中。2004年11月，现有系统的下载量超过7 000次，增加人脸检测功能将加强这一工具。更广泛地说，更好、更可靠的人脸检测是更好地进行人脸识别的关键垫脚石。胜任、可靠的人脸识别对于安全等许多应用都非常有价值。然而，良好的人脸识别的实用价值超越了安全应用-计算机更有可能与他们可以识别的人进行有益的互动。

Understanding Image-feature and Decision-procedure Choice for Human Face DetectionNSF Proposal 0413284AbstractFace detection algorithms have improved significantly in the last five years, in part because of a shift away from handcrafted systems to supervised learning systems that discover effective feature combinations from large amounts of training data. This broad characterization covers three prominent algorithms: the Viola and Jones Ada Boost algorithm, the statistical model approach of Schneiderman, and the SNoW algorithm of Yang et al. However, beyond this broad characterization, these algorithms differ greatly in detail. In particular, they use different image-features and different decision-procedures for determining whether a face is present. Why do these very different algorithms all perform relatively well, and how do the image-feature and decision-procedure choices implicit in these algorithms interact? This study will reveal the underlying fundamentals of these three approaches and others by establishing a common framework for combining and comparing image-feature extraction and decision-procedures. Empirical advances will be made in characterizing face detection tasks in an algorithm independent fashion critical to the development of better face detection algorithms. The practical impact of this research is two-fold. New open source versions of three prominent face detection algorithms will be developed and integrated into the current CSU Face Identification Evaluation System. Downloads of the current system exceeded 7,000 in November 2004, and the inclusion of face detection will enhance this tool. More broadly, better, more reliable face detection is a key stepping stone to better face recognition. Competent, reliable face recognition is highly valuable for many applications such as security. However, the practical value of good face recognition transcends security applications -- computers are more likely to interact helpfully with people they can recognize.