人脸检测算法的回顾和比较

K. Dang, Shanu Sharma
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引用次数: 80

摘要

随着视频和图像数据库的大量增加,智能系统对数据的自动理解和检查变得非常需要,因为人工操作变得越来越遥不可及。将其缩小到一个特定的领域,可以在图像中跟踪的最具体的对象之一是人,即面孔。人脸检测在越来越多的应用中成为一个挑战。它是人脸识别、人脸分析和人脸其他特征检测的第一步。本文讨论和分析了各种人脸检测算法,如Viola-Jones、SMQT特征和SNOW分类器、基于神经网络的人脸检测和基于支持向量机的人脸检测。采用DetEval软件对人脸周围边界框的精确值进行处理,并根据计算的查全率和查全率进行比较,得到准确的检测结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Review and comparison of face detection algorithms
With the tremendous increase in video and image database there is a great need of automatic understanding and examination of data by the intelligent systems as manually it is becoming out of reach. Narrowing it down to one specific domain, one of the most specific objects that can be traced in the images are people i.e. faces. Face detection is becoming a challenge by its increasing use in number of applications. It is the first step for face recognition, face analysis and detection of other features of face. In this paper, various face detection algorithms are discussed and analyzed like Viola-Jones, SMQT features & SNOW Classifier, Neural Network-Based Face Detection and Support Vector Machine-Based face detection. All these face detection methods are compared based on the precision and recall value calculated using a DetEval Software which deals with precised values of the bounding boxes around the faces to give accurate results.
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