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Description: 这是个图象中人脸识别的程序，对人的五官有很好的识别和勾画！-This is the image Face Recognition procedures, the features of a very good identification and sketches!
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Size: 3055616 |
Author: zhou |
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Description: 在本设计中源于将图像看作随机场的实例,同时从信号处理的观点出发，对图像进行统计描述。通过K-L变换降低人脸特征的维数，提取人脸图像的代数特征，并排序。这样，利用代数特征的比较，可从人脸库中识别一幅人脸。-in the design of the images from the airport as with the examples, from the viewpoint of signal processing, right image descriptive statistics. Through K-L transform facial features to reduce the dimension, extracting facial image algebraic characteristics, and sorting. Thus, the use of algebra, from the human face an identifiable face.
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Size: 15360 |
Author: hhh |
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Description: 針對單一人臉做出五官的檢測定位,並在人臉上畫出五官的方框作為表示.-For a single human face detection features make positioning, and his face painted in the box as an express facial features.
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Size: 3072 |
Author: 李毅駿 |
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Description: 人脸检测系统具有以下功能： 1.人脸检测，包括灰度图像和彩色图像，以及USB摄像头实时人脸检测。 2.人脸识别，包括静态人脸识别和USB摄像头实时人脸识别-Face detection system has the following features: 1. Human Face Detection, including gray-scale images and color images, as well as the USB camera in real-time face detection. 2. Face Recognition, including the static face recognition and face recognition in real-time USB camera
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Size: 243712 |
Author: 李成 |
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Description: 人脸检测和人的五官定位，好几种实现方法。-Face detection and facial features of the positioning of several methods.
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Size: 17287168 |
Author: 胡恩良 |
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Description: 人脸检测和人的五官定位!很不错的网站!好好好好好!-Face detection and location of facial features! Very good site! Good good good!
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Size: 243712 |
Author: 李丽 |
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Description: NMFs算法（带稀疏度约束的非负稀疏矩阵分解）用于实现基于人脸局部特征的人脸识别，通过近似的矩阵分解进行空间降维。-NMFS algorithm (sparse with degree-constrained non-negative sparse matrix factorization) for the realization of human faces based on local features of face recognition, through approximate matrix factorization space dimensionality reduction.
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Size: 23336960 |
Author: heying |
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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.
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Size: 4096 |
Author: yuzhun21 |
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Description: 人脸识别五官定位,光线补偿,去除人脸其它部分,画出五官-Face Recognition features positioning, light compensation, removal of other parts of face, painted facial features
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Size: 3409920 |
Author: 颜瑞 |
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Description: 人脸检测和跟踪学位论文；人脸检测定位：运动区域检测预处理， 肤色区域的分割和利用二值形态学等操作的人脸粗检，利用眼、嘴特征的彩色信息检测的人脸细检，利用投影法检测眼睛和嘴巴亮块的人脸定位。提出了一种基于自适应肤色模型匹配与验证的方法对人脸进行跟踪-Face detection and tracking dissertation Face Detection positioning: Sports regional detection pretreatment, color region segmentation and the use of binary morphological operations, such as human faces rough seizure, the use of eyes, mouth features detection of color information Face Fine Frederick, using projection method bright eyes and mouth block face positioning. A model based on adaptive color matching and validation of the method of face tracking
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Size: 3622912 |
Author: 田卉 |
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Description: 对含有人脸面部特征进行提取，可以有效的提取出人脸的面部信息，并且进行保存，同时，还有对话框界面-With the Department of Human face features extraction, the extract can effectively face the face of information, and save at the same time, have the dialog box interface
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Size: 74752 |
Author: 要强 |
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Description: 人脸识别算法源码，该算法用于提取人脸中的非线性特征-Source face recognition algorithm, the algorithm used to extract facial features of the non-linear
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Size: 2048 |
Author: 唐金良 |
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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.
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Size: 1586176 |
Author: chiaks |
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Description: Deaf people use facial expressions a non-manual channel for conveying grammatical information in sign language. Tracking facial features using the Kanade - Lucas - Tomasi (KLT) algorithm is a simple and effective method toward recognizing these facial expressions, which are performed simultaneously with head motions and hand signs. To make the tracker robust under these conditions, a Bayesian framework was developed as a feedback mechanism to the KLT tracker. This mechanism relies on a set of face shape subspaces learned by Probabilistic Principal Component Analysis. An update scheme was utilized to modify these subspaces and adapt to persons with different face shapes. The result shows that the proposed system can track facial features with large head motions, substantial facial deformations, and temporary face occlusions by hand.-Deaf people use facial expressions as a non-manual channel for conveying grammatical information in sign language. Tracking facial features using the Kanade- Lucas- Tomasi (KLT) algorithm is a simple and effective method toward recognizing these facial expressions, which are performed simultaneously with head motions and hand signs. To make the tracker robust under these conditions, a Bayesian framework was developed as a feedback mechanism to the KLT tracker. This mechanism relies on a set of face shape subspaces learned by Probabilistic Principal Component Analysis. An update scheme was utilized to modify these subspaces and adapt to persons with different face shapes. The result shows that the proposed system can track facial features with large head motions, substantial facial deformations, and temporary face occlusions by hand.
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Size: 189440 |
Author: Ng Jack |
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Description: 人脸视频图像编码是近年来图像编码领域里的一个研究热点问题,它在通信、互联网等方面有着广泛的应用前景。人脸图像编码的研究包含很多子问题,主要的三个方面为:精确的人脸目标定位算法,实时的人脸目标跟踪算法和高效的人脸图像编码方法。本文的主要研究工作在于: 1) 提出帧间差分和背景差分相结合的人脸目标定位算法,在人脸特征选择上,主要使用形状特征,通过椭圆拟合来得到人脸的位置。 2) 采用基于Kalman滤波的运动预测方法对人脸的区域变化进行预测,以此缩小目标可能存在的区域(ROI).-Facial video image coding image compression in recent years a hot research topic in the field of the problem, it is in the communication, the Internet have a wide range of applications. Coding of face images contain many sub-problems, the main three are: accurate target location algorithm for face, real-time face tracking algorithm and efficient facial image coding method. This major research work are: 1) of the frame difference and background difference goal of combining face localization algorithm, feature selection in the human face, the main use of shape features, by ellipse fitting to get the location of the face. 2) Kalman filtering based motion prediction method changes the face of the region predicted to narrow the possible target area (ROI).
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Size: 1024 |
Author: 周参 |
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Description: 基于人脸结构特征的眼睛定位算法论文，值得学习。-Based on structural features of the eyes, face localization algorithm paper, worth learning.
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Size: 397312 |
Author: mao |
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Description: Face Detection using local SMQT features and split up SnoW classifier
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Size: 3454976 |
Author: heyudi |
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Description: 一个用Java做的人脸识别程序，
功能比较强大，大家可以做个参考-A face recognition program to use Java to do, features more powerful, we can be a reference
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Size: 2104320 |
Author: sad |
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Description: Paper on automatic face contour detection.
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Size: 390144 |
Author: |
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Description: 此文的目的有三个：第一，当地连续均值量化变换特征是提出照明和传感器敏感操作在目标识别上。其次，注册稀疏Winnows网络分割，提出了加快原分类。最后，特点和分类相结合对于正面人脸检测任务。检测结果列
为MIT + CMU系统和BioID数据库。关于这人脸检测器，接收器操作特征曲线BioID数据库产生最好的结果公布。对于结果麻省理工学院的中央结算系统+数据库相当于国家的最先进的脸探测器。一个人脸检测算法的MATLAB版本可以从http://www.mathworks.com/matlabcentral/fileexchange/
loadFile.do？的ObjectID = 13701＆的objectType =FILE下载。
-The purpose of this paper is threefold: firstly, the local Successive
Mean Quantization Transform features are proposed for illumination
and sensor insensitive operation in object recognition. Secondly, a
split up Sparse Network of Winnows is presented to speed up the
original classifier. Finally, the features and classifier are combined
for the task of frontal face detection. Detection results are presented
for the MIT+CMU and the BioID databases. With regard to this
face detector, the Receiver Operation Characteristics curve for the
BioID database yields the best published result. The result for the
CMU+MIT database is comparable to state-of-the-art face detectors.
A Matlab version of the face detection algorithm can be downloaded
from http://www.mathworks.com/matlabcentral/fileexchange/
loadFile.do?objectId=13701&objectType=FILE.
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Size: 1397760 |
Author: 霞 |
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