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Description: 这个是进行人脸识别或是人眼定位所需要用到的BioID人脸数据库
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Author: 正 |
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Description: 这个是进行人脸识别或是人眼定位所要用到的BioID人连数据库，该数据库是进行过归一化处理后的
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Description: 这个是进行人脸识别或是人眼定位所需要用到的BioID人脸数据库-This is carried out face recognition or positioning required by the human eye is used BioID face database
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Size: 13307904 |
Author: 正 |
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Description: 这个是进行人脸识别或是人眼定位所要用到的BioID人连数据库，该数据库是进行过归一化处理后的-This is carried out face recognition or positioning by using the human eye to even BioID database, which is conducted normalized after treatment
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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.
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Size: 871424 |
Author: Ng Jack |
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Description: BioID-FD-Eyepos-V1.2
人臉資料庫
-BioID-FD-Eyepos-V1.2
face database
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Size: 153600 |
Author: Rong |
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Description: 最小邻域均值投影函数及其在眼睛定位算法.提出一种投影函数：最小邻域均值投影函数．该函数通过计算每条投影线上各像素点邻域均值的最小值
来跟踪图像中的低灰度特征．与传统的积分投影函数和方差投影函数相比，它以求最小值的局部选择性代替传统投
影函数的全局累加性，因此具有对片状噪声不敏感的特点、此外，在计算过程中，它还能记录最小值点的二维位置信
息，是一个二维的搜索算子、最小邻域均值投影函数的这些特点使其非常适合于眼睛定位．它对眼睛，特别是瞳孔，总
能够产生精确、鲁棒的响应通过在CAS—PEAL数据库和BioID数据库上的实验表明，其定位正确率与精确度均高
于传统的投影函数．-A projection function called minimal neighborhood mean projection function(MNMPF)is proposed．
The projection function calculates and stores the minimal neighborhood mea1]of each pixel on each projection line，
SO that it is able to trace the low grayscale features in image．Compared with traditional projection functions，i．e．
integral projection function(IPF)and variance projection function(VPF)，MNMPF is insensitive to sheet noise，due
to the local selectivity of its mimmum operation．During the computation of MNMPF,the image locations of
minima are recorded at the same time．This makes MNMPF a 2D operator．All these properties of MNM PF are very
suitable for eye location．It can bring precise and robust response to eyes，especially pupils．Experiments on
CAS—PEAL and BioID databases show its excellent correct rate and precision over traditional projection functions．
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Size: 436224 |
Author: 郭事业 |
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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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Description: 本文应用SMQT和 SPLIT UP SNOW 分类器来完成对人脸的检测。-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.
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Size: 1561600 |
Author: 吴绪周 |
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