2024年6月15日发(作者：)

成都理工大学2014届学士学位论文（设计）

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：

1) 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。

2) 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。

3) 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。

4) 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

成都理工大学2014届学士学位论文（设计）

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 of

computer 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 basis of the PCA as a improved mance is

compared between the two, it is concluds that the real time and accuracy of the latter

is greater than the y the Adaboost face detection algorithm and 2DPCA

are combined, which not only can greatly reduce the recognition time, but also

complement each other, effectively improve the recognition rate.

Key words：Face recognition 2DPCA Feature extraction Face detection