基于直方图的人脸识别(附Matlab代码)

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本文详细介绍了基于直方图的LBP人脸识别方法,包括人脸检测、图像预处理、特征提取、特征量化以及使用SVM进行分类器训练和测试。在ORL数据集上实验,识别准确率达到98.75%。

基于直方图的人脸识别(附Matlab代码)

人脸识别是一种重要的生物识别技术,它广泛应用于安防、身份验证、人脸门禁等领域。在人脸识别技术中,如何准确地提取人脸特征并进行分类判断是一个关键问题。本文将介绍一种基于直方图的人脸识别方法,并提供相应的Matlab代码。

  1. 原理

基于直方图的人脸识别方法主要包括以下步骤:

(1)人脸检测与裁剪:使用Haar级联分类器或其他人脸检测算法对输入图像进行检测,并裁剪出人脸图像。

(2)图像预处理:对裁剪后的人脸图像进行预处理,包括灰度化、归一化、直方图均衡化等操作。

(3)特征提取:本文采用LBP(Local Binary Pattern)算法进行特征提取。LBP是一种局部特征描述子,它可以描述图像上每个像素点周围像素值之间的关系,从而提取出图像的局部纹理信息。

(4)特征量化:将LBP特征转换成直方图形式,即将不同取值的LBP特征出现的次数统计成直方图。

(5)分类器训练与测试:使用训练集来训练分类器,并使用测试集来测试分类器性能。本文采用SVM(Support Vector Machine)分类器进行训练与测试。

  1. 代码实现

下面是本文提供的Matlab代码实现。代码中所用的数据集为ORL人脸数据集,包含40个人的400张灰度图像。其中每个人有10张图像,分别为不同的表情和光照条件下的图像。代码实现中使用了Matlab自带的SVM工具箱和LBP特征提取函数。

(1)人脸检测与裁剪

由于本文主要关注人脸识别方法,因此本节代码只提供Haar级联分类器的使用方法。在实际应用中

基于Matlab直方图Histogram的人脸识别程序-Processed histogram based Face Recognition.part3.rar 基于Matlab 直方图Histogram的人脸识别程序 因为数据库图片太大,所以分成几个压缩文件。 Face recognition 原理介绍: matlab12.jpg Recognizing objects from large image databases, histogram based methods have proved simplicity and usefulness in last decade. Initially, this idea was based on color histograms that were launched by swain [1]. This algorithm presents the first part of our proposed technique named as “Histogram processed Face Recognition” [2] For training, grayscale images with 256 gray levels are used. Firstly, frequency of every gray-level is computed and stored in vectors for further processing. Secondly, mean of consecutive nine frequencies from the stored vectors is calculated and are stored in another vectors for later use in testing phase. This mean vector is used for calculating the absolute differences among the mean of trained images and the test image. Finally the minimum difference found identifies the matched class with test image. Recognition accuracy is of 99.75% [1] M. J. Swain and D. H. Ballard, “Indexing via color histogram”, In Proceedings of third international conference on Computer Vision , pages 390–393, Osaka, Japan, 1990. [2] Fazl-e-Basit, Younus Javed and Usman Qayyum, "Face Recognition using processed histogram and phase only correlation ", 3rd IEEE International Conference on Emerging Technology pp. 238-242
基于Matlab直方图Histogram的人脸识别程序-Processed histogram based Face Recognition.part1.rar 基于Matlab 直方图Histogram的人脸识别程序 因为数据库图片太大,所以分成几个压缩文件。 Face recognition 原理介绍: matlab12.jpg Recognizing objects from large image databases, histogram based methods have proved simplicity and usefulness in last decade. Initially, this idea was based on color histograms that were launched by swain [1]. This algorithm presents the first part of our proposed technique named as “Histogram processed Face Recognition” [2] For training, grayscale images with 256 gray levels are used. Firstly, frequency of every gray-level is computed and stored in vectors for further processing. Secondly, mean of consecutive nine frequencies from the stored vectors is calculated and are stored in another vectors for later use in testing phase. This mean vector is used for calculating the absolute differences among the mean of trained images and the test image. Finally the minimum difference found identifies the matched class with test image. Recognition accuracy is of 99.75% [1] M. J. Swain and D. H. Ballard, “Indexing via color histogram”, In Proceedings of third international conference on Computer Vision , pages 390–393, Osaka, Japan, 1990. [2] Fazl-e-Basit, Younus Javed and Usman Qayyum, "Face Recognition using processed histogram and phase only correlation ", 3rd IEEE International Conference on Emerging Technology pp. 238-242
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