Description: 针对人脸识别中图像前期处理的人脸图像归一化问题，用AdaBoost Cascade方法检测出人脸和眼裂，提出以两眼裂中心点连线为基准进行水平旋转和尺度的归一化矫正方法。与通常的利用两眼瞳距方法相比较，该方法更加快速准确，能处理多达20°的倾斜，对戴眼镜、图像模糊、俯视、仰视、斜视等情况的鲁棒性更好。
A novel approach was presented, using the line between the two center points of the palpebral fissures of two eyes to normalize the face images for the image pretreatment of face recognition. The AdaBoost Cascade method was used to detect faces and palpebral fissures. It could deal with tilt from 0°to 20°, and get more robust outcomes than that using the line between two pupils commonly in dealing with the image being veiled, overlooking, looking up, sidelong and having on glasses. Platform: |
Size: 614400 |
Author:lichaoyou |
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Description: 这是一篇现在主要的基于adaboost算法的文章，详细讲述了怎么运用该算法做人脸检测，欢迎下载！-This is now a major adaboost algorithm based on the article, described in detail how the use of the human face detection algorithm, download welcome! Platform: |
Size: 107520 |
Author:程艳花 |
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Description: 用帧间差法及Adaboost算法实现人脸检测，可以增加人脸检测的效率-Using inter-frame difference method and the Adaboost face detection algorithm, it can increase the efficiency of face detection Platform: |
Size: 2004992 |
Author:萧枫 |
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Description: 人脸检测的一个程序,adaboost+cascade+haar
能运行-Face detection of a program, adaboost+ Cascade+ Haar to run Platform: |
Size: 786432 |
Author:who |
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Description: 基于adaboost的构造的cascade用于快速检测人脸，能够实时检测视频中的人脸。没有采用opencv的代码。-Adaboost based on the structure of the cascade for the rapid detection of human faces, real-time video can face. Did not use the code opencv. Platform: |
Size: 1528832 |
Author:wangdi |
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Description: 这片论文描述了动态物体的特征跟踪，用到了15个框架。拥有很强的适应性和跟踪能力。作为人脸识别，模式识别，动态跟踪的开发人员，有很好的参考价值。用c++编写，如果用OpenCV更好-This paper describes a visual object detection framework that is capable of processing
images extremely rapidly while achieving high detection rates. There are three
key contributions. The first is the introduction of a new image representation called the
“Integral Image” which allows the features used by our detector to be computed very
quickly. The second is a learning algorithm, based on AdaBoost, which selects a small
number of critical visual features and yields extremely efficient classifiers [4]. The
third contribution is a method for combining classifiers in a “cascade” which allows
background regions of the image to be quickly discarded while spending more computation
on promising object-like regions. A set of experiments in the domain of face
detection are presented. The system yields face detection performance comparable to
the best previous systems [16, 11, 14, 10, 1]. Implemented on a conventional desktop,
face detection proceeds at 15 frames per second Platform: |
Size: 784384 |
Author:lai |
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Description: 基于Adaboost级联分类的人脸和人眼检测，其中的三个xml是Adaboost分类器参数-Adaboost cascade based on the human face and eye detection, three of which are Adaboost classifier parameters xml Platform: |
Size: 162816 |
Author:kiser_cai |
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Description: 基于haar特征的adaboost级联分类器的人脸识别应用，可直接运行-Adaboost cascade haar feature based classifier for face recognition applications can be run directly Platform: |
Size: 4583424 |
Author:苏军 |
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Description: 训练好的人脸分类器，可直接载入运行，基于adaboost的级联分类器-Trained face classifier, can be directly loaded to run, based on cascade classifier adaboost Platform: |
Size: 4944896 |
Author:苏军 |
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Description: 基于ADABOOST的人脸检测代码，使用继承的策略，包括特征的计算，提取，ADABOOST训练，级联，最后训练出数据，还有注释，可以帮助大家研究ADBOOST-face detection code based on ADABOOST using inheritance strategies, and it includs the calculation and extraction of features, the training and cascade of ADABOOST, and the data will be trained out at last,annotations is also given as well , it can help you research ADBOOST Platform: |
Size: 13312 |
Author:薛 |
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Description: 基于adaboost的构造的cascade用于快速检测人人脸，能够实时检测视频中的人脸。没有采用opencv的代码。
-Based on the cascade of adaboost constructed for rapid detection of all face, real-time detection of the face in the video. Of opencv code. Platform: |
Size: 1371136 |
Author:卓越 |
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Description: 一种机器视觉的物体检测算法，是一种先进物体检测算法，全英文描述.讲述一种机器视觉的物体检测算法的实现-his paper describes a visual object detection framework that is capable of processing images extremely
rapidly while achieving high detection rates. There are three key contributions. The fi rst is the introduction
of a new image representation called the “Integral Image” which allows the features used by our detector
to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small
number of critical visual features and yields extremely effi cient classifi ers [6]. The third contribution is a
method for combining classifi ers in a “cascade” which allows background regions of the image to be quickly
discarded while spending more computation on promising object-like regions. A set of experiments in the
domain of face detection are presented. The system yields face detection performace comparable to the best
previous systems [18, 13, 16, 12, 1]. Implemented on a conventional desktop, face detection proceeds at 15
frames Platform: |
Size: 402432 |
Author:huyongjin |
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Description: this codes is Source code for face detection of viola paper.of its Features is:
Feature Computation: The “Integral” image representation
Feature Selection: The AdaBoost training algorithm .
Real-timeliness: A cascade of classifiers.-this codes is Source code for face detection of viola paper.of its Features is:
Feature Computation: The “Integral” image representation
Feature Selection: The AdaBoost training algorithm .
Real-timeliness: A cascade of classifiers.
Platform: |
Size: 420864 |
Author:fatemeh |
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Description: 基于dsp的人脸识别，使用harr特征和adaboost级联分类器(Based on dsp face recognition, use the harr feature and the adaboost cascade classifier) Platform: |
Size: 3505152 |
Author:devil00
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Description: 人脸检测：
第一部分，使用Harr-like特征表示人脸，使用“ 积分图”实现特征数值的快速计算;
第二部分， 使用Adaboost算法挑选出一些最能代表人脸的矩形特征( 弱分类器)，按照加权投票的方式将弱分类器构造为一个强分类器;
第三部分， 将训练得到的若干强分类器串联组成一个级联结构的层叠分类器，级联结构能有效地提高分类器的检测速度。(Face detection:
In the first part, the Harr-like feature is used to represent the human face, and the "integral graph" is used to realize the fast calculation of feature values;
In the second part, the Adaboost algorithm is used to select some rectangular features (weak classifiers) that represent the human face, and the weak classifier is constructed into a strong classifier according to weighted voting;
In the third part, a series of strong classifiers are formed in series to form a cascade classifier with cascading structure, and the cascade structure can effectively improve the detection speed of classifier.) Platform: |
Size: 2048 |
Author:14024235
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Search - adaboost cascade face