面向身份认证的人脸识别及应用
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摘要
人脸识别技术是生物特征识别领域中最为典型的应用之一,在监控系统、公共安全以及家居娱乐等方面有着广泛的应用前景。但人脸识别技术在实际应用中还有诸多问题需要解决,如人脸特征点的精确定位、人脸归一化、高效的特征提取方法以及鲁棒的识别算法等。本文针对以上提出的关键问题从以下几个方面开展了深入和全面的研究:
(1)提出了一种基于局部梯度算子的嘴角自动检测和定位方法。通过Adaboost算法检测出人脸图像大致的嘴部区域,采用局部梯度算子提取嘴部轮廓,利用Ostu阈值法对提取的轮廓进行二值化处理,根据链码跟踪最终确定左右嘴角的精确位置。最后依据两眼与嘴角的定位结果对人脸进行旋转和双向尺寸缩放处理。实验结果分析表明,基于局部梯度算子能快速准确的检测和定位嘴部,对表情和噪声的影响具有比较高的鲁棒性,能够更加精确的对人脸进行归一化处理。
(2)在特征提取方面,传统局部二值模式(LBP)算子存在不足:直方图维数过长、鉴别能力不强、分块方法不够合理。针对以上问题,提出双一维局部二值模式(DULBP)算子,DULBP算子相比传统LBP算子具有以下几个优势:(a)DULBP算子通过改变数据的排列方式,大大降低了特征维数;(b)DULBP算子将比较中心像素变为比较邻域内所有像素的均值,增强了中心像素的作用而且有益于提高鉴别能力;(c)多层的分块模式更加合理的兼顾了全局信息和局部信息。实验结果分析证明DULBP相对于传统的LBP、ULBP具有更佳的特征描述能力。
(3)提出了一种基于粒子群优化支持向量机(PSO-SVM)的人脸识别方法。支持向量机的参数选择一直是解决分类问题的难点。本章提出了一种在人脸识别中采用粒子群优化算法选择支持向量机参数的方法。最后探讨了基于粒子群优化支持向量机方法和传统支持向量机、BP神经网络在FERET人脸库上的识别率和识别速度。
(4)开发并实现了一个人脸识别算法的仿真平台,详细介绍了该平台各模块的功能。本文部分算法成功应用于2008年第29届北京奥运会开幕式场馆出入控制系统和人脸识别查询系统,最后给出了大型场馆出入控制系统的测试结果。
Face recognition technique is one of the most representative applications in the area of biometrics recognition. It has a wide application in the monitoring system, public security, and home entertainment. However, there are many problems need to be solved before face recognition technique can be applied in practice, such as facial landmark location, efficient feature extraction methods and robust recognition algorithms. Therefore, this thesis extensively studies the above-mentioned problems. The main contributions include:
(1) A fast and accurate method of automatic detection and location of the mouth corner was proposed. At first, the mouth region is roughly detected by Adaboost in a face image. Then, the mouth contour is detected by Local Gradient Operator and binarized by Ostu threshold method and the mouth corners are accurately localized by Chain code tracing. Finally, the face image is rotated, and normalized by integrating the location of eyes and the corner of the mouth in the vertical and horizontal directions. The experiment results indicate that this method can detect and locate mouth corners fast and exactly. It can reduce the influence of facial expression and posture, and show better normalization performance.
(2) In the aspect of feature extraction, the conventional LBP operators have several disadvantages, such as rather much dimension of histograms, lower discrimination and unreasonable blocking-way. Aimed at the problems, the Double one-dimension Uniform Local Binary Pattern (DULBP) operator was proposed. DULBP operator has several advantages: a)DULBP operator reduces significantly the feature’s dimensionality though changing the pattern of the data arrange; b)Its discrimination is strengthened due to considering the center pixel point; c)layered blocking-way gives consideration to the global information and the local information. Experimental results demonstrate that DULBP has a higher recognition rate than the conventional LBP and ULBP.
(3) A face recognition method based on support vector machine and particle swarm optimization was proposed. Parameters selection is an important problem in the research of classification. The method based on support vector machine and particle swarm optimization was proposed in the face recognition application. The experimental indicates that PSO-SVM has a higher face recognition accuracy than the conventional SVM, BPNN.
(4) A platform for new algorithm development and simulation was developed and realized, and the function of every module was introduced in detail. The successful application of some face recognition algorithms in an access control system of the 29th Beijing Olympic Game and a query system based on face recognition technique. At last, the performance of an access control system was given.
(1)提出了一种基于局部梯度算子的嘴角自动检测和定位方法。通过Adaboost算法检测出人脸图像大致的嘴部区域,采用局部梯度算子提取嘴部轮廓,利用Ostu阈值法对提取的轮廓进行二值化处理,根据链码跟踪最终确定左右嘴角的精确位置。最后依据两眼与嘴角的定位结果对人脸进行旋转和双向尺寸缩放处理。实验结果分析表明,基于局部梯度算子能快速准确的检测和定位嘴部,对表情和噪声的影响具有比较高的鲁棒性,能够更加精确的对人脸进行归一化处理。
(2)在特征提取方面,传统局部二值模式(LBP)算子存在不足:直方图维数过长、鉴别能力不强、分块方法不够合理。针对以上问题,提出双一维局部二值模式(DULBP)算子,DULBP算子相比传统LBP算子具有以下几个优势:(a)DULBP算子通过改变数据的排列方式,大大降低了特征维数;(b)DULBP算子将比较中心像素变为比较邻域内所有像素的均值,增强了中心像素的作用而且有益于提高鉴别能力;(c)多层的分块模式更加合理的兼顾了全局信息和局部信息。实验结果分析证明DULBP相对于传统的LBP、ULBP具有更佳的特征描述能力。
(3)提出了一种基于粒子群优化支持向量机(PSO-SVM)的人脸识别方法。支持向量机的参数选择一直是解决分类问题的难点。本章提出了一种在人脸识别中采用粒子群优化算法选择支持向量机参数的方法。最后探讨了基于粒子群优化支持向量机方法和传统支持向量机、BP神经网络在FERET人脸库上的识别率和识别速度。
(4)开发并实现了一个人脸识别算法的仿真平台,详细介绍了该平台各模块的功能。本文部分算法成功应用于2008年第29届北京奥运会开幕式场馆出入控制系统和人脸识别查询系统,最后给出了大型场馆出入控制系统的测试结果。
Face recognition technique is one of the most representative applications in the area of biometrics recognition. It has a wide application in the monitoring system, public security, and home entertainment. However, there are many problems need to be solved before face recognition technique can be applied in practice, such as facial landmark location, efficient feature extraction methods and robust recognition algorithms. Therefore, this thesis extensively studies the above-mentioned problems. The main contributions include:
(1) A fast and accurate method of automatic detection and location of the mouth corner was proposed. At first, the mouth region is roughly detected by Adaboost in a face image. Then, the mouth contour is detected by Local Gradient Operator and binarized by Ostu threshold method and the mouth corners are accurately localized by Chain code tracing. Finally, the face image is rotated, and normalized by integrating the location of eyes and the corner of the mouth in the vertical and horizontal directions. The experiment results indicate that this method can detect and locate mouth corners fast and exactly. It can reduce the influence of facial expression and posture, and show better normalization performance.
(2) In the aspect of feature extraction, the conventional LBP operators have several disadvantages, such as rather much dimension of histograms, lower discrimination and unreasonable blocking-way. Aimed at the problems, the Double one-dimension Uniform Local Binary Pattern (DULBP) operator was proposed. DULBP operator has several advantages: a)DULBP operator reduces significantly the feature’s dimensionality though changing the pattern of the data arrange; b)Its discrimination is strengthened due to considering the center pixel point; c)layered blocking-way gives consideration to the global information and the local information. Experimental results demonstrate that DULBP has a higher recognition rate than the conventional LBP and ULBP.
(3) A face recognition method based on support vector machine and particle swarm optimization was proposed. Parameters selection is an important problem in the research of classification. The method based on support vector machine and particle swarm optimization was proposed in the face recognition application. The experimental indicates that PSO-SVM has a higher face recognition accuracy than the conventional SVM, BPNN.
(4) A platform for new algorithm development and simulation was developed and realized, and the function of every module was introduced in detail. The successful application of some face recognition algorithms in an access control system of the 29th Beijing Olympic Game and a query system based on face recognition technique. At last, the performance of an access control system was given.
引文
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