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face recognition using hmm. first version.
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微软的基于HMM的人脸识别原代码, 非常经典的说-Microsoft HMM-based Face Recognition source, said the very classic
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HMM用于人脸识别，从OPENCV里提取出来的,非常经典的说-HMM for face recognition, OPENCV extract from the very classical said
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采用OpenCV进行人脸识别，隐马尔科夫链的应用，由混合分量来分割HMM的每个内在状态的所有观测值，运用现有的图像观测值分割为所有嵌入和内部的HMM函数，计算可能的变换矩阵-Using OpenCV for face recognition, hidden Markov chain applications, from the mixed components to split the internal state of each HMM all observations, use of the
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二维线性鉴别分析(2DLDA)算法能有效解决线性鉴别分析(LDA)算法的“小样本”效应，支持向量机
(SVM)具有结构风险最小化的特点，将两者结合起来用于人脸识别。首先，利用小波变换获取人脸图像的低频分量，忽
略高频分量：然后，用2DLDA算法提取人脸图像低频分量的线性鉴别特征，用“一对多”的SVM 多类分类算法完成人脸
识别。基于ORL人脸数据库和Yale人脸数据库的实验结果验证了2DLDA+SVM算法应用于人脸识别的有效性。-”Small sample size”problem
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HMM, face detection, recognition
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This paper presents a hybrid framework of feature extraction and hidden Markov modeling (HMM) for two-dimensional pattern recognition. Importantly, we explore a new discriminative training criterion to assure model compactness and discriminability. T
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基于hmm的人脸识别的matlab程序，可以成功运行，对了解该算法有很大的帮助-HMM based on the face recognition of matlab program, can be successful operation, to understand the algorithm has very great help
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利用隐马尔科夫方法进行人脸识别，具有较高的识别精度-use the HMM to human face recognition
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使用hmm隐马尔科夫链做人脸识别，重量级论文。非常好。
face recognition using an embedded H-face recognition using an embedded HMM
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an efficient system for face recognition based on hidden markov models and SVD as features extractor
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A New Fast and Efficient HMM-Based Face Recognition System Using a 7-State HMM Along With SVD Coefficients
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