Description: 子空间学习的代码，主要包括人脸识别中常用的特征提取算法如pca lda 以及目前常见的流行学习的相关代码-Subspace learning the code, mainly including commonly used in face recognition feature extraction algorithms such as pca lda and the current prevalence of common learning-related code Platform: |
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Author:dd |
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Description: 本文设计基于DCT的人脸识别系统，首先结合当今人脸识别的背景和发展状况讨论了人脸识别的研究内容及在各方面的应用；然后研究了人脸识别进行预处理，讨论了人脸识别预处理的其他方法，分析各种方法的利弊，最后采用DCT（离散余弦变换）实现人脸图像预处理中的降维处理；接下来对人脸图像的特征提取进行了研究，简单叙述了几何特征提取和代数特征提取，同时深入研究了基于DCT和PCA变换的人脸图像特征提取，从而实现是否对人脸识别系统识别率有所提高的研究；对于分类器的选择，本文对两种分类器进行了探讨，即最近邻分类器和BP神经网络分类器，同时采用BP神经网络分类器作为本次基于DCT的人脸识别系统设计的分类器，并对BP神经网络进行分类的算法进行设计，BP神经网络具有学习功能，只要采用本系统对训练图片进行训练，就可以记下图像的相关信息，对于测试图片，就可以很准确的识别出该图片是属于哪个的。最后，本文对整个人脸识别系统设计实验进行了实验分析，实验结果表明本文采用的方法切实有效。- This is the design of the DCT-based face recognition systems. First of all, the background light of the current face recognition and face recognition to discuss the development of research in all aspects of content and applications And then studied the pretreatment of face recognition to discuss the pre-treatment of other face recognition methods,and analysis of the pros and cons of various methods, finally, the use of DCT (discrete cosine transform) image pre-processing to achieve in the face of the reduced-order processing Next on the face image feature extraction have been studied, A brief description of the feature extraction and algebraic geometry feature extraction, while in-depth study based on the DCT and the PCA Transform face image feature extraction in order to achieve face recognition system to identify whether the rate of increase in the research For the choice of classifier, this paper carried out on two of classifier, that is, nearest neighbor classifier and BP neura Platform: |
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Author:刘文珍 |
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Description: lda线性特征提取，用于人脸识别，首先进行小波特征提取后用lda提取特征。-lda linear feature extraction for face recognition, first of all, after feature extraction using wavelet feature extraction using lda. Platform: |
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Author:gu |
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Description: 基于高波滤波的特征提取技术，从而实现人脸识别和表情识别的目的-Based on high-wave filter feature extraction techniques, in order to achieve the purpose of face recognition and expression recognition Platform: |
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Author:hefangfang |
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Description: 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. This criterion is derived from the hypothesis test theory via maximizing the confidence of accepting the hypothesis that observations are from target HMM states rather than competing HMM states. Accordingly, we develop the maximum confidence hidden Markov modeling (MC-HMM) for face recognition. Platform: |
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Author:rupesh |
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Description: 计算机人脸识别技术( Face Reocgnition)就利用计算机分析人脸图像,从中提取出有效的识别信息,用来辨认身份的一门技术。[ 1 ]即对已知人脸进行标准化处理后,通过某种方法和数据库中的人脸样本进行匹配,寻找库中对应人脸及该人脸相关信息。人脸自动识别系统有两个主要技术环节,一是人脸定位,即从输入图像中找到人脸存在的位置,将人脸从背景中分割出来,二是对标准化后的人脸图像进行特征提取和识别。本文中介绍的PCA (特征脸)方法就是一种常用的人脸
特征提取方法。-Computer Face Recognition Technology (Face Reocgnition) on the use of computer analysis of facial image, to extract the valid identification information used to identify the status of a technology. [1] that is known to standardize treatment of face, through a method and a database of face samples for matching, search library, the corresponding face and the face-related information. Automatic face recognition system has two main technical aspects, first, face location, that is, from the input image to find the location of the face there, the faces will be split out from the background, the second is, the standard features of face images extraction and recognition. Described in this paper PCA (Eigenfaces) method is a common facial feature extraction method. Platform: |
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Author:Highjoe |
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Description: 完整的表情识别系统一般包括人脸表情图像捕获、预处理、人脸检测与定位、
人脸分割与归一化、人脸表情特征提取、人脸表情识别。本文着重研究了人脸表
情特征提取、特征选择及表情分类等关键问题，并提出了一些改进的方法，同时
进行了仿真实验-Complete expression recognition systems typically include facial expression image capture, preprocessing, face detection and location, face segmentation and normalization, facial expression feature extraction, facial expression recognition. Paper focuses on the facial expression feature extraction, feature selection and classification of facial expressions and other key issues and put forward a number of improved methods, and simulation experiment Platform: |
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Author:KW |
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Description: This thesis relates to the design, implementation and evaluation of statis¬ tical face recognition techniques. In particular, the use of Hidden Markov Models in various forms is investigated as a recognition tool and critically evaluated. Current face recognition techniques are very dependent on issues like background noise, lighting and position of key features (ie. the eyes, lips etc.). Using an approach which specifically uses an embedded Hidden Markov Model along with spectral domain feature extraction techniques, shows that these dependencies may be lessened while high recognition rates are maintained. Platform: |
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Author:ivan |
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Description: An automatic facial feature extraction method is
presented in this paper. The method is based on the edge
density distribution of the image. In the preprocessing stage a
face is approximated to an ellipse, and genetic algorithm is
applied to search for the best ellipse region match. In the
feature extraction stage, genetic algorithm is applied to
extract the facial features, such as the eyes, nose and mouth,
in the predefined sub regions. The simulation results
validates that the proposed method is capable of
automatically extracting features from various video images
effectively under natural lighting environments and in the
presence of certain amount of artificial noise and of multi-
face oriented with angles.-An automatic facial feature extraction method is
presented in this paper. The method is based on the edge
density distribution of the image. In the preprocessing stage a
face is approximated to an ellipse, and genetic algorithm is
applied to search for the best ellipse region match. In the
feature extraction stage, genetic algorithm is applied to
extract the facial features, such as the eyes, nose and mouth,
in the predefined sub regions. The simulation results
validates that the proposed method is capable of
automatically extracting features from various video images
effectively under natural lighting environments and in the
presence of certain amount of artificial noise and of multi-
face oriented with angles. Platform: |
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Author:fais |
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Description: 图像识别系统的结构与工作原理，在对图像预处理、特征提取、分类、图像匹配算法进行深入研究和分
析的基础上，分析和比较了各种算法的优缺点-Image Recognition System structure and working principle, in the image preprocessing, feature extraction, classification, image matching algorithm in-depth research and analysis, based on the analysis and comparison of the advantages and disadvantages of various algorithms Platform: |
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Author:kohsa |
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Description: 这是一个用vc++编写的人脸识别系统，输入一张普通的人脸图像，通过对人脸进行预处理，特征提取，以及模板匹配等操作，最后作出识别结果-This is a written by vc++ face recognition system, input a regular face image preprocessing, through to the face, feature extraction, and template matching operation, and finally make the results Platform: |
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Author:edison |
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Description: 采用PCA对人脸特征进行抽取，用SVM多累分类器对人脸进行识别，有操作界面-Using PCA for facial feature extraction, and more tired with the SVM classifier for face identification, a user interface Platform: |
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Author:钟佩 |
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Description: 这是对人脸的特征提取，再把提取出来的特征跟模板库比较，最后识别出结果。-This is the human face feature extraction, and then extracted features compare with the template library, the final recognition result. Platform: |
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Author:qianhongli |
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Description: Haar特征提取，广泛运用于人脸识别，指纹识别等图像识别领域。-HAAR feature extraction, is widely used in the field of face recognition, fingerprint identification, image recognition. Platform: |
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Author:zhaodan |
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Description: 本例子中有40人脸库，详尽的人脸特征提取例子，大家可以参考学习。-This example has 40 face , detailed facial feature extraction example, we can refer to learn. Platform: |
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Author:张瑜 |
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Search - face feature extraction