基于回歸算法的人臉識別分類器設(shè)計(jì)
發(fā)布時(shí)間：2018-11-09 09:56
【摘要】：目前,人臉識別技術(shù)已經(jīng)被應(yīng)用于我們的日常生活當(dāng)中的某些領(lǐng)域,但是該技術(shù)在手機(jī)端身份驗(yàn)證和支付這些場景中的應(yīng)用還沒普及,一方面是由于人臉識別準(zhǔn)確度面部姿勢、光照、表情變化影響較大,另一方面是目前已有的對復(fù)雜環(huán)境下的人臉分類效果好的分類算法計(jì)算量太大,不適合應(yīng)用在內(nèi)存較小的手機(jī)端。因此,本文的研究目的是改進(jìn)計(jì)算量小的分類器設(shè)計(jì),提升人臉在不穩(wěn)定條件下的人臉識別率。在模式識別領(lǐng)域中,基于最近鄰分類器提出的最近特征面分類器(Nearest Feature Plane,NFP)和線性回歸分類器(Linear Regression Classifier,LRC)是兩種計(jì)算量小且分類識別較好的分類器,具備被應(yīng)用于手機(jī)端的條件。因此,本課題對線性和非線性回歸分類器的算法思想進(jìn)行了詳細(xì)的分析和研究,基于線性與非線性回歸分類器的算法思想提出了幾種分類器的設(shè)計(jì)方法。本文的研究方法和成果包括以下幾個(gè)方面,針對分類過程中易錯(cuò)分點(diǎn)的分類不精確性問題,基于線性回歸分類器的算法思想,提出了三種改進(jìn)的分類器,分別為基于點(diǎn)線距離分類器、偽線空間分類器和距離受限分類器。三種新方法分別利用了樣本點(diǎn)與回歸直線之間的距離信息及樣本點(diǎn)間的空間特性對線性回歸算法進(jìn)行改進(jìn),大量的對比實(shí)驗(yàn)表明新方法可以有效提升分類器在光照、角度和表情變化下易錯(cuò)分人臉圖像的識別率。同時(shí),基于核函數(shù)和最近特征面分類器,提出了一種加核最近最遠(yuǎn)分類器和基于中心受角度限制的最近特征面分類器。其中,加核最近最遠(yuǎn)分類器利用核函數(shù)能有效地解決樣本非線性可分問題的優(yōu)勢,將核函數(shù)與最近最遠(yuǎn)線性回歸分類器結(jié)合,有效地提升了原樣本空間中非線性可分樣本的分類準(zhǔn)確性。而基于最近特征面的通過增加角度來改進(jìn)由于特征面的無限延長而引起的交叉樣本錯(cuò)分問題,通過將兩種分類器在多個(gè)標(biāo)準(zhǔn)人臉庫上與其他的改進(jìn)方法進(jìn)行對比實(shí)驗(yàn),驗(yàn)證了兩種新方法對易錯(cuò)分交叉樣本的分類準(zhǔn)確性。
[Abstract]:At present, face recognition technology has been used in some areas of our daily life, but the application of this technology in mobile phone authentication and payment of these scenes has not been widely used, on the one hand, because of the face recognition accuracy of facial posture, The change of illumination and expression has a great influence on the face classification. On the other hand the existing classification algorithms which have good effect on face classification in complex environment have too much computation and are not suitable for the mobile phone with small memory. Therefore, the purpose of this paper is to improve the classifier design with small computational complexity, and to improve the face recognition rate under unstable conditions. In the field of pattern recognition, the nearest feature surface classifier (Nearest Feature Plane,NFP) and the linear regression classifier (Linear Regression Classifier,LRC) proposed by the nearest neighbor classifier are two kinds of classifiers with small computation and good classification recognition. Has the condition to be applied to the mobile phone. Therefore, the algorithm of linear and nonlinear regression classifier is analyzed and studied in detail. Based on the algorithm of linear and nonlinear regression classifier, several design methods of classifier are proposed. The research methods and achievements of this paper include the following aspects. Aiming at the problem of the inaccuracy of the classification of error-prone points in the classification process, three improved classifiers are proposed based on the idea of linear regression classifier. It is based on point line distance classifier, pseudo line space classifier and distance limited classifier respectively. The three new methods make use of the distance information between the sample points and the regression lines and the spatial characteristics of the sample points respectively to improve the linear regression algorithm. A large number of comparative experiments show that the new method can effectively improve the illumination of the classifier. The recognition rate of error-prone face image under the change of angle and expression. At the same time, based on kernel function and nearest feature surface classifier, a kernel nearest furthest classifier and a nearest feature surface classifier based on center angle are proposed. The kernel nearest farthest classifier combines the kernel function with the nearest farthest linear regression classifier by utilizing the advantage of kernel function to solve the nonlinear separable problem of samples effectively. The classification accuracy of nonlinear separable samples in the original sample space is improved effectively. Based on the most recent feature surface by adding angle to improve the cross-sample misclassification problem caused by the infinite extension of the feature surface, two classifiers are compared with other improved methods on multiple standard face databases. The accuracy of the two new methods for the classification of error-prone cross samples is verified.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
本文編號：2320076
[Abstract]:At present, face recognition technology has been used in some areas of our daily life, but the application of this technology in mobile phone authentication and payment of these scenes has not been widely used, on the one hand, because of the face recognition accuracy of facial posture, The change of illumination and expression has a great influence on the face classification. On the other hand the existing classification algorithms which have good effect on face classification in complex environment have too much computation and are not suitable for the mobile phone with small memory. Therefore, the purpose of this paper is to improve the classifier design with small computational complexity, and to improve the face recognition rate under unstable conditions. In the field of pattern recognition, the nearest feature surface classifier (Nearest Feature Plane,NFP) and the linear regression classifier (Linear Regression Classifier,LRC) proposed by the nearest neighbor classifier are two kinds of classifiers with small computation and good classification recognition. Has the condition to be applied to the mobile phone. Therefore, the algorithm of linear and nonlinear regression classifier is analyzed and studied in detail. Based on the algorithm of linear and nonlinear regression classifier, several design methods of classifier are proposed. The research methods and achievements of this paper include the following aspects. Aiming at the problem of the inaccuracy of the classification of error-prone points in the classification process, three improved classifiers are proposed based on the idea of linear regression classifier. It is based on point line distance classifier, pseudo line space classifier and distance limited classifier respectively. The three new methods make use of the distance information between the sample points and the regression lines and the spatial characteristics of the sample points respectively to improve the linear regression algorithm. A large number of comparative experiments show that the new method can effectively improve the illumination of the classifier. The recognition rate of error-prone face image under the change of angle and expression. At the same time, based on kernel function and nearest feature surface classifier, a kernel nearest furthest classifier and a nearest feature surface classifier based on center angle are proposed. The kernel nearest farthest classifier combines the kernel function with the nearest farthest linear regression classifier by utilizing the advantage of kernel function to solve the nonlinear separable problem of samples effectively. The classification accuracy of nonlinear separable samples in the original sample space is improved effectively. Based on the most recent feature surface by adding angle to improve the cross-sample misclassification problem caused by the infinite extension of the feature surface, two classifiers are compared with other improved methods on multiple standard face databases. The accuracy of the two new methods for the classification of error-prone cross samples is verified.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
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