【摘要】：人臉識(shí)別技術(shù)是一種精度高、穩(wěn)定性好、使用方便的生物識(shí)別技術(shù),市場(chǎng)應(yīng)用前景廣闊。然而,人臉識(shí)別技術(shù)頻繁受到假冒攻擊(或復(fù)制攻擊),仍存在諸多安全隱患。在抵抗假冒攻擊(或復(fù)制攻擊)方面,活體檢測(cè)具有顯著的效果,它對(duì)樣本是否具有生命特征進(jìn)行了辨識(shí)。針對(duì)人臉識(shí)別系統(tǒng)無(wú)法識(shí)別采集的人臉圖像是否來(lái)自真人的問(wèn)題,本文重點(diǎn)研究了基于2D人臉圖像和3D人臉深度圖的活體檢測(cè)算法。主要工作包括:1、針對(duì)現(xiàn)有3D人臉活體檢測(cè)數(shù)據(jù)庫(kù)較少的問(wèn)題,本文采集了一個(gè)RGBD人臉數(shù)據(jù)庫(kù)。該數(shù)據(jù)庫(kù)正樣本包括使用Kinect和另一雙目設(shè)備采集的104個(gè)真人在0.5-2米處不同姿態(tài)的深度人臉數(shù)據(jù),共計(jì)20973張圖片。負(fù)樣本包括使用Kinect采集在不同環(huán)境下0.5-2米處不同角度的ipad、電腦、手機(jī)、照片攻擊人臉,共計(jì)12300張圖片。2、針對(duì)現(xiàn)有的傅里葉頻譜分析方法較為簡(jiǎn)單且準(zhǔn)確率較低的情況,本文提出了一種改進(jìn)的傅里葉頻譜特征方法。該方法在對(duì)2D人臉區(qū)域圖像提取二維離散傅里葉頻譜圖的基礎(chǔ)上,加入分塊子空間的方法,將傅里葉頻譜圖分成若干個(gè)子塊,并求得每一個(gè)子塊內(nèi)圖像的平均能量值,歸一化后級(jí)聯(lián)成一個(gè)全局傅里葉頻譜特征向量。實(shí)驗(yàn)結(jié)果表明,改進(jìn)后傅里葉頻譜特征能有效地提高2D人臉圖像的活體檢測(cè)準(zhǔn)確率。3、針對(duì)在訓(xùn)練樣本增加時(shí),基于傅里葉頻譜特征的2D人臉活體檢測(cè)準(zhǔn)確率會(huì)進(jìn)一步下降的情況,本文提出了融合LBP特征的FS-LBP特征人臉活體檢測(cè)方法。該方法將傅里葉頻譜特征和低維的LBP特征級(jí)聯(lián),并使用SVM來(lái)分類(lèi)判別。實(shí)驗(yàn)結(jié)果表明,該方法在2D人臉活體檢測(cè)上更優(yōu)于時(shí)下最主流的MSLBP特征方法。4、針對(duì)灰度共生矩陣緯度低,且其3D人臉的活體檢測(cè)率仍可進(jìn)一步提升的情況,本文提出了一種多尺度灰度共生矩陣的方法。該方法首先通過(guò)對(duì)RGB圖像進(jìn)行人臉檢測(cè)并同步采集深度圖的人臉區(qū)域圖像,其次將人臉區(qū)域深度圖調(diào)整為不同尺度大小的深度圖像,并分別提取其灰度共生矩陣特征,并級(jí)聯(lián)成一個(gè)多尺度灰度共生矩陣特征,最后使用SVM來(lái)分類(lèi)判別。實(shí)驗(yàn)結(jié)果表明,該方法在3D人臉深度圖上的活體檢測(cè)準(zhǔn)確率高于灰度共生矩陣特征和LBP特征方法。最后對(duì)本文工作進(jìn)行了總結(jié),并對(duì)本文后續(xù)工作進(jìn)行了展望。
[Abstract]:Face recognition is a kind of biometric technology with high precision, good stability and convenient use. However, face recognition technology is frequently subjected to fake attacks (or copy attacks), there are still many security risks. In the aspect of resisting counterfeiting attack (or replica attack), in vivo detection has remarkable effect, and it identifies whether the sample has life characteristic or not. In order to solve the problem of whether the human face image can not be recognized by the face recognition system, this paper focuses on the living body detection algorithm based on 2D face image and 3D face depth map. The main work is as follows: 1. Aiming at the lack of 3D human face detection database, this paper collects a RGBD face database. The database includes 104 human face data with different pose depth at 0.5-2 meters collected using Kinect and another binocular device, with a total of 20973 images. The negative samples include ipad, computers, mobile phones and photos that use Kinect to collect 0.5-2 meters of different angles in different environments to attack faces, with a total of 12300 images. In view of the simple and low accuracy of the existing Fourier spectrum analysis methods, an improved Fourier spectrum feature method is proposed in this paper. On the basis of extracting 2D discrete Fourier spectrum from 2D face region image, the method of adding block subspace is used to divide the Fourier spectrum into several sub-blocks, and the average energy value of the image in each sub-block is obtained. After normalization, it is cascaded into a global Fourier spectrum eigenvector. Experimental results show that the improved Fourier spectrum features can effectively improve the accuracy of 2D face image in vivo detection. The accuracy of 2D human face detection based on Fourier spectrum features will be further reduced. In this paper, a FS-LBP feature based face detection method based on LBP features is proposed. In this method, Fourier spectrum features and low-dimensional LBP features are concatenated, and SVM is used to classify and discriminate. The experimental results show that this method is better than the most popular MSLBP feature method in 2D face detection. 4. Aiming at the low latitude of gray level co-occurrence matrix and the fact that its 3D face detection rate can be further improved. In this paper, a method of multi-scale gray level co-occurrence matrix is presented. The method firstly detects the face of RGB image and synchronously collects the facial region image of the depth map. Secondly, the depth map of the face region is adjusted to the depth image of different scales, and its gray level co-occurrence matrix feature is extracted respectively. And cascaded into a multi-scale gray level co-occurrence matrix feature, finally using SVM to classify discrimination. Experimental results show that the accuracy of this method is higher than that of gray level co-occurrence matrix and LBP features on 3D face depth images. Finally, the work of this paper is summarized, and the future work of this paper is prospected.
【學(xué)位授予單位】：集美大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41

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本文編號(hào)：2376059