基于SIFT的人脸识别算法研究与实现

发布时间：2018-04-12 16:32

本文选题：人脸识别 + 人脸检测　；参考：《河南理工大学》2016年硕士论文

【摘要】：科学技术的不断发展,带动着经济的快速发展,同时对各种公共场合身份识别和认定提出了新的更高的要求。而传统的通过人身标识物品和人身标识知识方式对身份辨别的方法已不能满足社会的需要。随之出现的生物特征识别技术,因其具有不易丢失、无法复制及安全性更高等优点,在人脸图像自动识别领域上得到广泛应用。人脸识别系统采用计算机对应用场景中摄像机所采集的图像进行人脸检测、分析、识别,从而确定图像中人的身份,实现辨别人脸的目的。此识别方法可为需要进行身份验证或跟踪的场合提供更加安全、快捷且有效验证方式。由此各种人脸识别算法被提出,其中由David Lowe所提出的SIFT(Scale Invariant Feature Transform)算法,因其提取的特征向量具有尺度不变旋转特性、鲁棒性强等优点,不断被应用于人脸识别研究领域。本文将针对基于YCbCr空间肤色模型、基于SIFT的人脸图像特征提取以及特征匹配开展深入研究,主要工作如下:1.针对前期的人脸检测和定位,提出一种基于YCbCr空间改进的肤色模型人脸检测算法。首先,建立肤色模型进行人脸肤色的提取,进而通过面积比以及人脸长宽比筛选出图像中的人脸;其次,为去除可能存在的耳部区域以及颈部区域干扰,总结一种耳根定位算法和一种基于人脸图像中心线像素灰度循环扫描方法。实验结果证明了此方法的准确性和有效性。2.针对SIFT算法所提取的特征点数量多,匹配难度大等问题,本文提出一种改进的SIFT算法来对人脸图像进行特征匹配:通过前期的人脸检测算法去除人脸图像中无关干扰,从而避免无关特征点的提取与匹配,保证特征点的有效性和准确性;3.在特征向量匹配阶段对特征向量进行分区匹配,然后用最小欧氏距离作为判断特征点是否匹配的准则,最后以最小欧氏距离与次最小欧氏距离之比作为有效匹配的效验,进而提高匹配效率和准确度。
[Abstract]:The continuous development of science and technology drives the rapid development of economy, and puts forward new and higher requirements for the identification and identification of various public places at the same time.However, the traditional method of identity identification by means of personal identification objects and personal identification knowledge can not meet the needs of society.The following biometric recognition technology has been widely used in the field of face image automatic recognition because of its advantages such as easy to lose, unable to copy and high security.The face recognition system uses the computer to detect, analyze and recognize the image collected by the camera in the application scene, so as to determine the identity of the person in the image and realize the purpose of face recognition.This method can provide a more secure, fast and effective authentication method for situations where authentication or tracing is needed.The SIFT(Scale Invariant Feature transform algorithm proposed by David Lowe has been applied to face recognition for its advantages of scale invariant rotation and strong robustness.In this paper, the feature extraction and feature matching of facial images based on YCbCr spatial skin model and SIFT are studied. The main work is as follows: 1.An improved skin color model face detection algorithm based on YCbCr space is proposed for face detection and localization.Firstly, the skin color model is established to extract the skin color of the face, and then the human face in the image is screened by the area ratio and the aspect ratio of the face. Secondly, in order to remove the possible interference of the ear region and the neck region,This paper summarizes an ear root location algorithm and a gray level cyclic scanning method based on centreline pixels of face image.The experimental results show that the method is accurate and effective.In order to solve the problems such as the large number of feature points extracted by SIFT algorithm and the difficulty of matching, this paper proposes an improved SIFT algorithm to match the features of face image. The previous face detection algorithm is used to remove the irrelevant interference in the face image.Thus avoiding the extraction and matching of independent feature points and ensuring the validity and accuracy of feature points.In the phase of feature vector matching, the feature vector is partitioned, then the minimum Euclidean distance is used as the criterion to judge whether the feature point is matched or not, and finally the ratio of the minimum Euclidean distance to the sub-minimum Euclidean distance is taken as the effective matching effect.Then the matching efficiency and accuracy are improved.
【学位授予单位】：河南理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41

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