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公开(公告)号：US09836640B2
公开(公告)日：2017-12-05
申请号：US14623106
申请日：2015-02-16
Applicant: Huawei Technologies Co., Ltd.
Inventor： Yu Zhang , Zhenhua Chai , Zhijun Du
CPC classification number: G06K9/00288 , G06K9/00228 , G06K9/6257 , G06K2009/4666 , G06N99/005
Abstract: A face detector training method, a face detection method, and apparatuses are provided. In the present invention, during a training phase, a flexible block based local binary pattern feature and a corresponding second classifier are constructed, appropriate second classifiers are searched for to generate multiple first classifiers, and multiple layers of first classifiers that are obtained by using a cascading method form a final face detector; and during a detection phase, face detection is performed on a to-be-detected image by using a first classifier or a face detector that is learned during a training process, so that a face is differentiated from a non-face, and a face detection result is combined and output.
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2.发明申请公开(公告)号：US20150235074A1
公开(公告)日：2015-08-20
申请号：US14623106
申请日：2015-02-16
Applicant: Huawei Technologies Co., Ltd.
Inventor： Yu Zhang , Zhenhua Chai , Zhijun Du
CPC classification number: G06K9/00288 , G06K9/00228 , G06K9/6257 , G06K2009/4666 , G06N99/005
Abstract: A face detector training method, a face detection method, and apparatuses are provided. In the present invention, during a training phase, a flexible block based local binary pattern feature and a corresponding second classifier are constructed, appropriate second classifiers are searched for to generate multiple first classifiers, and multiple layers of first classifiers that are obtained by using a cascading method form a final face detector; and during a detection phase, face detection is performed on a to-be-detected image by using a first classifier or a face detector that is learned during a training process, so that a face is differentiated from a non-face, and a face detection result is combined and output.
Abstract translation: 提供了面部检测器训练方法，面部检测方法和装置。 在本发明中，在训练阶段期间，构建基于灵活块的局部二进制模式特征和对应的第二分类器，搜索合适的第二分类器以生成多个第一分类器，并且通过使用 级联方法形成最终的面部检测器; 并且在检测阶段期间，通过使用在训练处理期间学习的第一分类器或面部检测器对待检测图像执行面部检测，使得脸部与非脸部区分开，并且脸部 检测结果合并输出。
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3.发明授权公开(公告)号：US09582710B2
公开(公告)日：2017-02-28
申请号：US14558414
申请日：2014-12-02
Applicant: Huawei Technologies Co., Ltd.
Inventor： Yu Zhang , Zhijun Du , Minjie Wang
CPC classification number: G06K9/00295 , G06K9/00221 , G06K9/4614 , G06K9/6256 , G06K9/6257 , G06K2009/4666
Abstract: Embodiments of the present invention disclose methods for generating a strong classifier for face detection. The methods include determining, according to a size of a prestored image training sample, a parameter of weak classifier of the image training sample, obtaining a sketch value of each of the weak classifiers of the image training sample, calculating a weighted classification error of each of the weak classifiers according to the sketch value and an initial weight of the image training sample, obtaining at least one optimal weak classifier according to the weighted classification error, and generating a strong classifier for face detection according to the optimal weak classifiers. The embodiments of the present invention further disclose an apparatus for generating a strong classifier for face detection. The embodiments of the present invention have advantages of improving robustness of code against noise and reducing a false detection rate of face detection.
Abstract translation: 本发明的实施例公开了用于生成用于面部检测的强分类器的方法。 所述方法包括根据预先存储的图像训练样本的大小来确定图像训练样本的弱分类器的参数，获得图像训练样本的每个弱分类器的草图值，计算每个图像训练样本的加权分类误差 根据草图值和图像训练样本的初始权重，根据加权分类误差获得至少一个最优弱分类器，并根据最优弱分类器生成用于面部检测的强分类器。 本发明的实施例还公开了一种用于生成用于面部检测的强分类器的装置。 本发明的实施例具有改善代码对噪声的鲁棒性并降低面部检测的错误检测率的优点。
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