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    • 44. 发明授权
    • Pose-invariant face recognition system and process
    • US07127087B2
    • 2006-10-24
    • US10983194
    • 2004-11-05
    • Fu Jie HuangHong-Jiang ZhangTsuhan Chen
    • Fu Jie HuangHong-Jiang ZhangTsuhan Chen
    • G06K9/00G06K9/62
    • G06K9/00228G06K9/00288G06K9/6292
    • A face recognition system and process for identifying a person depicted in an input image and their face pose. This system and process entails locating and extracting face regions belonging to known people from a set of model images, and determining the face pose for each of the face regions extracted. All the extracted face regions are preprocessed by normalizing, cropping, categorizing and finally abstracting them. More specifically, the images are normalized and cropped to show only a persons face, categorized according to the face pose of the depicted person's face by assigning them to one of a series of face pose ranges, and abstracted preferably via an eigenface approach. The preprocessed face images are preferably used to train a neural network ensemble having a first stage made up of a bank of face recognition neural networks each of which is dedicated to a particular pose range, and a second stage constituting a single fusing neural network that is used to combine the outputs from each of the first stage neural networks. Once trained, the input of a face region which has been extracted from an input image and preprocessed (i.e., normalized, cropped and abstracted) will cause just one of the output units of the fusing portion of the neural network ensemble to become active. The active output unit indicates either the identify of the person whose face was extracted from the input image and the associated face pose, or that the identity of the person is unknown to the system.
    • 45. 发明授权
    • Automatic music mood detection
    • 自动音乐心情检测
    • US07115808B2
    • 2006-10-03
    • US11265685
    • 2005-11-02
    • Lie LuHong-Jiang Zhang
    • Lie LuHong-Jiang Zhang
    • G10H1/40G10H7/00G06F17/00
    • G10H1/00G10H2210/071G10H2240/085
    • A system and methods use music features extracted from music to detect a music mood within a hierarchical mood detection framework. A two-dimensional mood model divides music into four moods which include contentment, depression, exuberance, and anxious/frantic. A mood detection algorithm uses a hierarchical mood detection framework to determine which of the four moods is associated with a music clip based on the extracted features. In a first tier of the hierarchical detection process, the algorithm determines one of two mood groups to which the music clip belongs. In a second tier of the hierarchical detection process, the algorithm then determines which mood from within the selected mood group is the appropriate, exact mood for the music clip. Benefits of the mood detection system include automatic detection of music mood which can be used as music metadata to manage music through music representation and classification.
    • 系统和方法使用从音乐中提取的音乐特征来检测分层情绪检测框架内的音乐心情。 二维情绪模型将音乐分为四种情绪,包括满足感,抑郁症,繁荣感和焦虑/疯狂。 情绪检测算法使用分级情绪检测框架来基于提取的特征来确定四种情绪中的哪一种与音乐剪辑相关联。 在层次检测过程的第一层中,算法确定音乐剪辑所属的两个心情组之一。 在层次检测过程的第二层中,算法然后确定来自所选择的心情组中的哪个心情是音乐剪辑的适当的精确心情。 情绪检测系统的优点包括自动检测音乐心情,可用作音乐元数据,通过音乐表示和分类来管理音乐。
    • 48. 发明申请
    • Automatic music mood detection
    • 自动音乐心情检测
    • US20050211071A1
    • 2005-09-29
    • US10811281
    • 2004-03-25
    • Lie LuHong-Jiang Zhang
    • Lie LuHong-Jiang Zhang
    • G10H1/00G10H1/40G10H7/00
    • G10H1/00G10H2210/071G10H2240/085
    • A system and methods use music features extracted from music to detect a music mood within a hierarchical mood detection framework. A two-dimensional mood model divides music into four moods which include contentment, depression, exuberance, and anxious/frantic. A mood detection algorithm uses a hierarchical mood detection framework to determine which of the four moods is associated with a music clip based on the extracted features. In a first tier of the hierarchical detection process, the algorithm determines one of two mood groups to which the music clip belongs. In a second tier of the hierarchical detection process, the algorithm then determines which mood from within the selected mood group is the appropriate, exact mood for the music clip. Benefits of the mood detection system include automatic detection of music mood which can be used as music metadata to manage music through music representation and classification.
    • 系统和方法使用从音乐中提取的音乐特征来检测分层情绪检测框架内的音乐心情。 二维情绪模型将音乐分为四种情绪,包括满足感,抑郁症,繁荣感和焦虑/疯狂。 情绪检测算法使用分级情绪检测框架来基于提取的特征来确定四种情绪中的哪一种与音乐剪辑相关联。 在层次检测过程的第一层中,算法确定音乐剪辑所属的两个心情组之一。 在层次检测过程的第二层中,算法然后确定来自所选择的心情组中的哪个心情是音乐剪辑的适当的精确心情。 情绪检测系统的优点包括自动检测音乐心情,可用作音乐元数据,通过音乐表示和分类来管理音乐。