Description: 人脸检测和定位过程
主要是实现从静态图像中获取人脸的过程-face detection and localization process is mainly from a static image acquisition process Face Platform: |
Size: 3629056 |
Author:薛燕 |
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Description: 用Visual c++ 实现的人脸检测和定位的代码，其中结合了肤色信息，包括了人眼的定位-with Visual c achieve the Face Detection and localization of the code, which combines the color information, including the positioning of the eyes Platform: |
Size: 472064 |
Author:韩冰 |
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Description: 人脸定位程序，可以定位出一幅图像中的一个或多个人脸，准确率较高-face localization procedures, the definition can be an image of one or more individuals face higher accuracy rate Platform: |
Size: 68608 |
Author:杨益敏 |
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Description: 人脸定位和识别的完整MATLAB代码，在国外网站上偶然得到的-face localization and identification of the integrity of MATLAB code, the foreign web sites the chance Platform: |
Size: 249856 |
Author:fanxubo |
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Description: 一个人脸定位识别的程序，采用VC++ 6.0 开发。-Identification of a Human Face Localization procedures, the use of VC++ 6.0 development. Platform: |
Size: 468992 |
Author:龚辟愚 |
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Description: 人脸定位实例,提供的例程及算法，均可在Visual C++ 6.0下编译通过.可参照书籍了解程序的使用。-Human Face Localization examples provided routines and algorithms can be found in the Visual C++ 6.0 compiler passed under. Could understand the procedure with reference to the use of books. Platform: |
Size: 64512 |
Author:半半 |
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Description: C++编写的用于人脸定位和识别的程序，能识别和定位眼睛、鼻子。-C++ Prepared for Human Face Localization and identification procedures to identify and positioning of the eyes, nose. Platform: |
Size: 83968 |
Author:段西尧 |
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Description: Face_Location
基于肤色的精确人脸定位算法，能较为准确的定位彩色图像中的正面，小角度偏
侧和旋转的人脸，还能检测出一幅图中的多个人脸。-Face_Location based on the color precision face localization algorithm, can be more accurate positioning of color images in a positive, small-angle and rotation hemiparkinsonism Face, but also detected a map of multi-personal face. Platform: |
Size: 104448 |
Author:Scorpio |
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Description: A new approach toward target representation and localization, the central component in visual tracking
of non-rigid objects, is proposed. The feature histogram based target representations are regularized
by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions
suitable for gradient-based optimization, hence, the target localization problem can be formulated using
the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya
coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the
presented tracking examples the new method successfully coped with camera motion, partial occlusions,
clutter, and target scale variations. Integration with motion filters and data association techniques is also
discussed. We describe only few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking.-A new approach toward target representation and localization, the central component in visual trackingof non-rigid objects, is proposed. The feature histogram based target representations are regularizedby spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functionssuitable for gradient-based optimization, hence, the target localization problem can be formulated usingthe basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyyacoefficient as similarity measure, and use the mean shift procedure to perform the optimization. In thepresented tracking examples the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is alsodiscussed. We describe only few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking . Platform: |
Size: 2779136 |
Author: |
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Description: 人脸定位实例，边缘提取，标记眼睛，标记嘴巴鼻子-Human Face Localization examples, edge detection, marking the eyes, mouth and nose tag Platform: |
Size: 221184 |
Author:pengwei |
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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: |
Size: 1586176 |
Author:chiaks |
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Description: face detection
Face detection can be regarded as a more general case of face localization In face localization, the task is to find the locations and sizes of a known number of faces (usually one). In face detection, one does not have this additional information.
Early face-detection algorithms focused on the detection of frontal human faces, whereas newer algorithms attempt to solve the more general and difficult problem of multi-view face detection. That is, the detection of faces that are either rotated along the axis from the face to the observer (in-plane rotation), or rotated along the vertical or left-right axis (out-of-plane rotation),or both.-face detection
Face detection can be regarded as a more general case of face localization In face localization, the task is to find the locations and sizes of a known number of faces (usually one). In face detection, one does not have this additional information.
Early face-detection algorithms focused on the detection of frontal human faces, whereas newer algorithms attempt to solve the more general and difficult problem of multi-view face detection. That is, the detection of faces that are either rotated along the axis from the face to the observer (in-plane rotation), or rotated along the vertical or left-right axis (out-of-plane rotation),or both. Platform: |
Size: 13312 |
Author:gianni |
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Description: 人脸识别因其在安全验证系统、信用卡验证、医学、档案管理、视频会
议、人机交互、系统公安(罪犯识别等)等方面的巨大应用前景而越来越成为
当前模式识别和人工智能领域的一个研究热点。
本文提出了基于24位彩色图像对人脸进行识别的方法，介绍的主要内容是图像处理，它在整个软件中占有极其重要的地位，图像处理的好坏直接影响着定位和识别的准确率。本软件主要用到的图像处理技术是：光线补偿、高斯平滑和二值化。在识别前，先对图像进行补光处理，再通过肤色获得可能的脸部区域，最后根据人脸固有眼睛的对称性来确定是否就是人脸，同时采用高斯平滑来消除图像的噪声，再进行二值化，二值化主要采用局域取阈值方法，接下来就进行定位、提取特征值和识别等操作。经过测试，图像预处理模块对图像的处理达到了较好的效果，提高了定位和识别的正确率- Face recognition is a complex and difficult problem that is important for surveillance and security, telecommunications, digital libraries , video meeting, and human-computer intelligent interactions.
The paper introduced the method of face recognition that based on the 24 bit multicolor image, Main content that the paper introduced is the picture treatment, It occupies the extremely important position in the whole software, the quality of picture process directly influenced the accuracy rate of localization and discerning. The picture process technology that the software mainly used included : light compensating、gauss smooth and twain value method. before discerning, we compensated the light for image, then we could obtain the possible face area through the complexion, finally, the system could depend on the symmetry of eyes to make sure whether it is the face of people, at the same time, the system could eliminate noises through the method that named gauss smoothness, then we use Platform: |
Size: 2286592 |
Author:张雨 |
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Description: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects,
is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The
masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem
can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as
similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method
successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data
association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking. Platform: |
Size: 2459648 |
Author:Ali |
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Description: Real-Time Facial Feature Point Extraction-Localization of facial feature points is an important step for many subsequent facial image analysis tasks. In this paper, we proposed a new coarse-to-fine method for extracting 20 facial feature points from image sequences. In particular, the Viola-Jones face detection method is extended to detect small-scale facial components with wide shape variations, and linear Kalman filters are used to smoothly track the feature points by handling detection errors and head rotations. The proposed method achieved higher than 90 detection rate when tested on the BioID face database and the FG-NET facial expression database. Moreover, our method shows robust performance against the variation of face resolutions and facial expressions. Platform: |
Size: 871424 |
Author:Ng Jack |
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Description: 基于肤色的人脸定位检查系统设计，实现人脸检测-Face localization based on skin color inspection system design, implementation, Face Detection Platform: |
Size: 1165312 |
Author:renmin |
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Description: 实现人脸检测的功能，对合适大小的图像，将其放在相应的matlab文件夹下即可自动进行人脸检测并定位。-For face detection function, the size of the image on the right, put it in the appropriate folder automatically matlab for face detection and localization. Platform: |
Size: 1457152 |
Author:鱼雷 |
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Description: 实现一种结合颜色空间、变换及变形模板的自动唇部定位及唇轮廓提取、跟踪方法首先在空间建立肤色模型进行人脸检测、定位, 并由人脸几何特征进行唇部粗定位然后结合唇色模型进行变换使肤、唇色差别明显化, 提出根据亮度信息对变换结果预处理后用法进行图像分割, 经唇色模型进一步验证后实现唇部精定位再使用变形模板来进行嘴唇轮廓特征提取, 为增强内轮廓定位的鲁棒性, 对经亮度预处理和唇色模型验证得到的口腔区域边缘图进行曲线拟合来实现内轮廓定位最后, 将唇读图像序列中上一帧的唇部定位结果拓展后作为当前帧的预测区域再进行处理来实现唇动跟踪。-To achieve a combination of color space, transform and deformable template automatic lip localization and lip contour extraction, tracking the establishment of the first color model in the space of face detection, location, geometric features of the face by the rough location and then combined with lip lip transformation so that the skin color model, lip color difference visible, presented the results according to the luminance information to transform the image after pretreatment use of segmentation, and further validated by the lip model to achieve precise positioning and then use the deformation of the lip template for lip contour extraction , to enhance the robustness of location within the outline, by the brightness of the pretreatment and the lip model validation by oral Quyu edge map to fit curve to achieve the final positioning within the contour, the lip-reading image sequence in the last frame of the lips After positioning results extend the forecast area as the current frame Platform: |
Size: 970752 |
Author:郭事业 |
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Search - localization face