Description: 在DSP TMS320VC5402上实现的语音识别算法，使用代数汇编编程，编译器为CCS2.2。使用LPCC算法提取语音特征，DTW算法进行特征匹配-TMS320VC5402 DSP in the realization of speech recognition algorithm, the use of algebraic compilation of programming, compiler for DSP. LPCC Features extraction algorithm to use voice features, DTW feature matching algorithm Platform: |
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Author:彭洪 |
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Description: 本程序是基于模板匹配的语音识别技术。提取语音的特征，并建立模板库。可以将语音识别技术应用于机器人。-this program is based on template matching speech recognition technology. Extraction of voice features, and the establishment of Template Library. Voice recognition can be used in robot technology. Platform: |
Size: 84992 |
Author: |
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Description: 在DSP TMS320VC5402上实现的语音识别算法，使用代数汇编编程，编译器为CCS2.2。使用LPCC算法提取语音特征，DTW算法进行特征匹配-TMS320VC5402 DSP in the realization of speech recognition algorithm, the use of algebraic compilation of programming, compiler for DSP. LPCC Features extraction algorithm to use voice features, DTW feature matching algorithm Platform: |
Size: 98304 |
Author:彭洪 |
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Description: 为了更准确地识别人的表情，在识别人脸7 种基本表情（愤怒、厌恶、恐惧、高兴、无表情、悲伤和惊讶）时，采用了局域二值
模式技术提取面部特征，进行由粗略到精细的表情分类。在粗略分类阶段，7 种基本表情中的2 种表情被选为初步分类结果（候选表情）。
在精细分类阶段，选用计算加权卡方值确定最终分类结果。采用日本的Jaffe 表情数据库来验证算法性能，对陌生人表情的识别率为77.9％，
其结果优于采用同样数据库的其他方法，且易于实现-In order to more accurately identify the person s facial expressions, in the identification of seven kinds of basic facial expressions (anger, disgust, fear, happy, expressionless, sadness and surprise), the use of local binary pattern of facial features extraction techniques, carried out by the rough to the fine expression classification. In the rough classification stage, the seven kinds of basic expressions in the two kinds of expression was selected as the initial classification results (candidate expressions). In the fine classification stage, the choice of calculating the weighted chi-square value to determine the final classification results. Jaffe expressions used in Japan to validate algorithm performance database of strangers face recognition rate was 77.9, the result is better than using the same database in other ways, and are easy to achieve Platform: |
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Author:张波 |
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Description: 本程序包是关于图形处理和人脸识别的,包括人脸Gabor特征提取，canny算子，水线阈值方法等．大家可以一起参考-The package is on the graphics processing and face recognition, including face Gabor feature extraction, canny operator, waterline threshold methods. Together we can make reference Platform: |
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Author:单昊 |
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Description: RASTA is a program for performing feature extraction in speech
recognition systems. It takes files or streams of audio data and
produces files or streams of feature vectors. Three
feature extraction approaches are supported - PLP, log-rasta and
J-rasta. For each case, cepstral features are typically produced,
although there are some options for spectrum-like features.
RASTA handles various file formats and can be used in either
batch mode or an interactive demonstration system.
-RASTA is a program for performing feature extraction in speech
recognition systems. It takes files or streams of audio data and
produces files or streams of feature vectors. Three
feature extraction approaches are supported- PLP, log-rasta and
J-rasta. For each case, cepstral features are typically produced,
although there are some options for spectrum-like features.
RASTA handles various file formats and can be used in either
batch mode or an interactive demonstration system.
Platform: |
Size: 104448 |
Author:杨杭洲 |
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Description: 利用Sub-pattern PCA在Yale人脸库上进行人脸识别的matlab源代码，子模式主成分分析首先对原始图像分块，然后对相同位置的子图像分别建立子图像集，在每一个子图像集内使用PCA方法提取特征，建立子空间。对待识别图像，经相同分块后，分别将子图像向对应的子空间投影，提取特征。最后根据最近邻原则进行分类。-Sub-pattern PCA use in the Yale face database for face recognition on the matlab source code, sub-mode principal component analysis first of the original image block, and then the same sub-image, respectively, the location of the establishment of sub-image set, in each sub-image Set the use of PCA to extract the features, the establishment of sub-space. Treatment to identify images, by the same block, the respective sub-image to the corresponding sub-space projection, feature extraction. Finally, according to the principle of nearest neighbor classification. Platform: |
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Author:章格 |
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Description: 提出一种基于多纹理特征融合的新颖虹膜识别方法。该方法对虹膜图像做Gabor 小波变换
提取不同分辨力不同方向下的纹理特征作为虹膜的全局特征，在滤波后的子窗口图像上运用灰度
级共现矩阵(COM)提取虹膜的局部特征。通过加权欧几里德距离和最小距离分别对全局特征和局部
特征进行分类识别。-A texture feature fusion based on multi-novel iris recognition method. The method of iris image make Gabor wavelet transform extracted under different resolution in different directions as the texture feature of the overall characteristics of the iris, in the post-filter sub-window image using gray level co-occurrence matrix (COM) of the local feature extraction of the iris. Through the weighted Euclidean distance and minimum distance, respectively, on the global features and local features for classification. Platform: |
Size: 770048 |
Author:闫慧 |
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Description: 基于ORL人脸库的人脸识别的特征人脸的提取源代码。-Based on the ORL face database for face recognition facial features extraction source code. Platform: |
Size: 4096 |
Author:yuzhun21 |
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Description: 该软件最主要的功能就是要能识别出人脸，首先该系统需要对通过摄像头拍照而获取到的原始的人脸图片进行一系列处理才可进行下一步的工作，该处理过程也称图像预处理。预处理这个模块在整个人脸识别系统的开发过程中占有很重要的地位，只有预处理模块做的好，才可能很好的完成后面的人脸定位和特征提取这两大关键模块。-The main features of the software is to be able to identify the face, first of all, the system need to take pictures through the camera and access to the original face image can only be carried out a series dealing with the work of the next step, the process also known as image pretreatment. Pretreatment of the Face Recognition System module in the entire process of development plays an important role, and only do pre-processing module, and can be very good after the completion of the Human Face Localization and Feature Extraction of these two key modules. Platform: |
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Author:chiaks |
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Description: MATLAB 下的模式识别特征提取 用于对特征信号的提取-MATLAB under the feature extraction for pattern recognition features of the signal extraction Platform: |
Size: 237568 |
Author:w |
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Description: This paper identifies a novel feature space to
address the problem of human face recognition from
still images. This based on the PCA space of the
features extracted by a new multiresolution analysis
tool called Fast Discrete Curvelet Transform. Curvelet
Transform has better directional and edge
representation abilities than widely used wavelet
transform. Inspired by these attractive attributes of
curvelets, we introduce the idea of decomposing
images into its curvelet subbands and applying PCA
(Principal Component Analysis) on the selected
subbands in order to create a representative feature
set. Experiments have been designed for both single
and multiple training images per subject. A
comparative study with wavelet-based and traditional
PCA techniques is also presented. High accuracy rate
achieved by the proposed method for two well-known
databases indicates the potential of this curvelet based
feature extraction method.-This paper identifies a novel feature space to
address the problem of human face recognition from
still images. This is based on the PCA space of the
features extracted by a new multiresolution analysis
tool called Fast Discrete Curvelet Transform. Curvelet
Transform has better directional and edge
representation abilities than widely used wavelet
transform. Inspired by these attractive attributes of
curvelets, we introduce the idea of decomposing
images into its curvelet subbands and applying PCA
(Principal Component Analysis) on the selected
subbands in order to create a representative feature
set. Experiments have been designed for both single
and multiple training images per subject. A
comparative study with wavelet-based and traditional
PCA techniques is also presented. High accuracy rate
achieved by the proposed method for two well-known
databases indicates the potential of this curvelet based
feature extraction method. Platform: |
Size: 432128 |
Author:Swati |
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Description: 关于语音处理的英文书籍，其中特征提取部分（MFCC）讲解的很好很详细-The performance of speech recognition systems receiving speech that has been transmitted over mobile channels can be
significantly degraded when compared to using an unmodified signal. The degradations are as a result of both the low
bit rate speech coding and channel transmission errors. A Distributed Speech Recognition (DSR) system overcomes
these problems by eliminating the speech channel and instead using an error protected data channel to send a
parameterized representation of the speech, which is suitable for recognition. The processing is distributed between the
terminal and the network. The terminal performs the feature parameter extraction, or the front-end of the speech
recognition system. These features are transmitted over a data channel to a remote "back-end" recognizer. The end result
is that the transmission channel does not affect the recognition system performance and channel invariability is achieved. Platform: |
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Author:gqy |
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Description: ：针对传统Gabor变换在提取表情特征时，冗余较大、特征维数较高的不足，结合ASM
自动特征定位技术，提出了一种基于特征点Gabor特征和ASM 形状特征相融合的面部表情
识别方法．实验表明，两种特征的融合，可有效地利用特征点的局部纹理信息和脸部器官的整
体形状信息，达到了更好的面部表情识另4效果．-: Gabor transform traditional expression feature extraction, the redundancy large feature dimension is high enough, combined with ASM automatic feature location technique, the algorithm based on feature characteristics of Gabor features and the ASM shape fusion of facial expression recognition Methods. Experimental results show that the integration of two features, feature points can effectively use the local texture information and the overall shape of the face organs of information, to achieve a better effect of facial expression understanding the other 4. Platform: |
Size: 365568 |
Author:MJ |
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Description: 提出了基于特征融合和模糊核判别分析(FKDA)的面部表情识别方法。首先，从每幅人脸图像中手工定
位34个基准点，作为面部表情图像的几何特征，同时采用Gabor小波变换方法对每幅表情图像进行变换，并提取基
准点处的Gabor小波系数值作为表情图像的Gabor特征；其次，利用典型相关分析技术对几何特征和Gabor特征进
行特征融合，作为表情识别的输人特征；然后，利用模糊核判别分析方法进一步提取表情的鉴别特征；最后，采用最
近邻分类器完成表情的分类识别。通过在JAFFE国际表情数据库和Ekman“面部表情图片”数据库上的实验，证实
了所提方法的有效性。-Proposed based on feature fusion and fuzzy kernel discriminant analysis (FKDA) facial expression recognition. First, face images of each piece of hand-set
Bit 34 basis points, as the geometric features of facial expression images, while using Gabor wavelet transform method to transform the images of each piece of expression, and extraction-based
Quasi-point of the Gabor wavelet coefficients, as Gabor features of facial expression image second, using canonical correlation analysis on the geometric features and Gabor features into
Line feature fusion, as expression recognition of input features then, using fuzzy kernel discriminant analysis method to extract and further identification features of expression Finally, the most
Neighbor classifier to complete expression of the classification. International expression by JAFFE database and Ekman "facial image" database on the experiment, confirmed
The proposed method. Platform: |
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Author:MJ |
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Description: 运用于人脸识别中的特征提取方法：通过稀疏化特征向量（即使一些不重要的特征值为0），来减少运算量-Used in face recognition feature extraction methods: by sparse feature vector (even if some important features of value 0), to reduce the computation Platform: |
Size: 1878016 |
Author:wangjia |
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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: 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: The Principal component analysis, is a standard technique used for data reduction in statistical pattern recognition and signal processing
A common problem in statistical pattern recognition is feature selection or feature extraction. Feature selection is a process whereby a data space is transformed into a feature space that theory has exactly same dimension as the original data space. However the transformation is designed in such a way that the data set is represented by a reduced number of “effective features” and most of the intrinsic information content of the data or the data set undergoes a dimensionality reduction.
PCA Platform: |
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Author:binu |
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Description: Pattern recognition and Gesture recognition are the growing fields of research. Being a significant part in non verbal communication hand gestures are playing vital role in our daily life. Hand Gesture recognition system provides us an innovative,
natural, user friendly way of interaction with the computer which is more familiar to the human beings.
Its main steps are discussed in the following:
Image Segmentation
Orientation Detection
Features extraction
Thumb detection:
Finger region detection:
Euclidean distance:
Classification and bits generation Platform: |
Size: 2135040 |
Author:ahmed |
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Search - features extraction recognition