System and method for multi-view face detection

    公开(公告)号:US07050607B2

    公开(公告)日:2006-05-23

    申请号:US10091100

    申请日:2002-03-04

    IPC分类号: G06K9/00 G06K9/62

    摘要: A system and method for real-time multi-view (i.e. not just frontal view) face detection. The system and method uses a sequence of detectors of increasing complexity and face/non-face discriminating thresholds to quickly discard non-faces at the earliest stage possible, thus saving much computation compared to prior art systems. The detector-pyramid architecture for multi-view face detection uses a coarse-to-fine and simple-to-complex scheme. This architecture solves the problem of lengthy processing that precludes real-time face detection effectively and efficiently by discarding most of non-face sub-windows using the simplest possible features at the earliest possible stage. This leads to the first real-time multi-view face detection system which has the accuracy almost as good as the state-of-the-art system yet 270 times faster, allowing real-time performance.

    System and method for multi-view face detection

    公开(公告)号:US20060120572A1

    公开(公告)日:2006-06-08

    申请号:US11299504

    申请日:2005-12-12

    IPC分类号: G06K9/00 G06K9/36

    摘要: A system and method for real-time multi-view (i.e. not just frontal view) face detection. The system and method uses a sequence of detectors of increasing complexity and face/non-face discriminating thresholds to quickly discard non-faces at the earliest stage possible, thus saving much computation compared to prior art systems. The detector-pyramid architecture for multi-view face detection uses a coarse-to-fine and simple-to-complex scheme. This architecture solves the problem of lengthy processing that precludes real-time face detection effectively and efficiently by discarding most of non-face sub-windows using the simplest possible features at the earliest possible stage. This leads to the first real-time multi-view face detection system which has the accuracy almost as good as the state-of-the-art system yet 270 times faster, allowing real-time performance.

    System and method for multi-view face detection
    3.
    发明授权
    System and method for multi-view face detection 失效
    多视角人脸检测系统及方法

    公开(公告)号:US07324671B2

    公开(公告)日:2008-01-29

    申请号:US11299504

    申请日:2005-12-12

    IPC分类号: G06K9/00

    摘要: A system and method for real-time multi-view (i.e. not just frontal view) face detection. The system and method uses a sequence of detectors of increasing complexity and face/non-face discriminating thresholds to quickly discard non-faces at the earliest stage possible, thus saving much computation compared to prior art systems. The detector-pyramid architecture for multi-view face detection uses a coarse-to-fine and simple-to-complex scheme. This architecture solves the problem of lengthy processing that precludes real-time face detection effectively and efficiently by discarding most of non-face sub-windows using the simplest possible features at the earliest possible stage. This leads to the first real-time multi-view face detection system which has the accuracy almost as good as the state-of-the-art system yet 270 times faster, allowing real-time performance.

    摘要翻译: 用于实时多视图(即,不仅仅是前视图)面部检测的系统和方法。 该系统和方法使用增加复杂性和面部/非脸部鉴别阈值的检测器序列来尽可能快地丢弃非面部,从而与现有技术系统相比节省了大量的计算。 用于多视角人脸检测的检测器 - 金字塔架构使用粗略到精细和简单到复杂的方案。 该架构解决了长时间处理的问题,通过在最早阶段使用最简单的可能特征来废弃大多数非面子子窗口,从而有效且高效地排除了实时面部检测。 这导致了第一个实时多视角人脸检测系统,其精度几乎与最先进的系统一样好,但是快了270倍,从而允许实时性能。

    Method for boosting the performance of machine-learning classifiers
    4.
    发明申请
    Method for boosting the performance of machine-learning classifiers 有权
    提高机器学习分类器性能的方法

    公开(公告)号:US20050144149A1

    公开(公告)日:2005-06-30

    申请号:US11067284

    申请日:2005-02-25

    IPC分类号: G06K9/00 G06K9/62 G06F15/18

    摘要: A novel statistical learning procedure that can be applied to many machine-learning applications is presented. Although this boosting learning procedure is described with respect to its applicability to face detection, it can be applied to speech recognition, text classification, image retrieval, document routing, online learning and medical diagnosis classification problems.

    摘要翻译: 提出了可应用于许多机器学习应用的新颖的统计学习过程。 尽管针对其面部检测的适用性描述了这种增强学习过程,但它可以应用于语音识别,文本分类,图像检索,文档路由,在线学习和医学诊断分类问题。