文档介绍：学士学位论文
基于2DPCA的人脸识别算法研究
摘要
人脸识别技术是对图像和视频中的人脸进行检测和定位的一门模式识别技术,包含位置、大小、个数和形态等人脸图像的所有信息。由于近年来计算机技术的飞速发展,为人脸识别技术的广泛应用提供了可能,所以图像处理技术被广泛应用了各种领域。该技术具有广阔的前景,如今已有大量的研究人员专注于人脸识别技术的开发。本文的主要工作内容如下:
介绍了人脸识别技术的基础知识,包括该技术的应用、背景、研究方向以及目前研究该技术的困难,并对人脸识别系统的运行过程以及运行平台作了简单的介绍。
预处理工作是在原始0RL人脸库上进行的。在图像的预处理阶段,经过了图象的颜色处理,图像的几何归一化,图像的均衡化和图象的灰度归一化四个过程。所有人脸图像通过上述处理后,就可以在一定程度上减小光照、背景等一些外在因素的不利影响。
介绍了目前主流的一些人脸检测算法,本文采用并详细叙述了Adaboost人脸检测算法。Adaboost算法首先需要创建人脸图像的训练样本,再通过对样本的训练,得到的级联分类器就可以对人脸进行检测。
本文介绍了基于PCA算法的人脸特征点提取,并在PCA算法的基础上应用了改进型的2DPCA算法,对两者的性能进行了对比,得出后者的准确度和实时性均大于前者,最后将Adaboost人脸检测算法和2DPCA算法结合,不仅能大幅度降低识别时间,而且还相互补充,有效的提高了识别率。
关键词:人脸识别 2DPCA 特征提取人脸检测
2DPCA Face Recognition Algorithm Based on The Research
Abstract:Face recognition is a technology to detect and locate human face in an image or video streams,Including location, size, shape, number and other information of human face in an image or video to the rapid development puter operation speed makes the image processing technology has been widely applied in many fields in recent years. This paper's work has the following several aspects:
1)Explained the background, research scope and method of face recognition,and introduced the theoretical method of face recognition field in general.
2)The pretreatments work is based on the original ORL face database. In the image preprocessing stage, there are the color of the image processing, image geometric normalization, image equalization and image gray scale normalization four parts. After united processing, the face image is standard, which can eliminate the adverse effects of some external factors.
3)All kinds of face detection algorithm is introduced, and detailed describing the Adaboost algorithm for face detection. Through the Adaboost algorithm to create a training sample,then Training the samples of face image,and obtaining the cascade classifier to detect human face.
4)This paper introduces the facial feature points extraction based on PCA ,and 2DPCA is used on the