【摘要】：非法認證者可通過偽裝人臉獲得進入人臉識別系統(tǒng)的權限,為社會安全帶來威脅。因此,活體人臉檢測具有現(xiàn)實緊迫性。然而,現(xiàn)有文獻多為照片人臉攻擊方面的研究,對于視頻人臉攻擊,識別率不甚理想。3D卷積神經網絡(Convolutional Neural Network,CNN)是一個深層結構,能自動學到圖像的分布式特征表示;與2D卷積相比,能學到連續(xù)視頻幀的動作信息。本文結合3D卷積神經網絡的特性,提出利用3D卷積神經網絡實現(xiàn)視頻人臉偽裝檢測。本文的研究內容主要包括:(1)對現(xiàn)有活體人臉檢測算法做了深入研究,分析了2DCNN的弊端,只能對二維圖像進行卷積;現(xiàn)有活體人臉檢測算法多為手工提取特征,存在的缺點為:(1)手工設計特征只對某種類型的圖片有較好的識別率;(2)手工設計特征需要較高的專業(yè)知識。本文對視頻做卷積,首次提出將3D卷積神經網絡應用于活體人臉檢測。(2)針對兩個公開的人臉偽裝數(shù)據(jù)庫,設計合適的3D卷積神經網絡結構,包括網絡的層數(shù)、卷積核大小及數(shù)量。此外,根據(jù)每個網絡在時間維度中下采樣不同的先后順序設計了三種待選網絡(Late,Slow,Early)。通過實驗測試,比較每個網絡識別率高低與網絡參數(shù)的數(shù)量來選擇合適的網絡。(3)利用前面找到的最優(yōu)網絡,將REPLAY-ATTACK和CASIA-FASD人臉偽裝數(shù)據(jù)庫作為實驗對象,對不同幀數(shù)輸入進行實驗對比,尋找使分類器性能達到最佳的最優(yōu)輸入幀數(shù)。通過提取網絡最后全連接層學到的時間空間特征,訓練支持向量機(Support Vector Machine,SVM)分類器,實現(xiàn)真實人臉和偽裝人臉的分類。(4)將最優(yōu)網絡和最優(yōu)幀數(shù)作為輸入,實現(xiàn)兩個公開人臉偽裝數(shù)據(jù)庫REPLAY-ATTACK和CASIA-FASD的多尺度內部數(shù)據(jù)庫測試和交叉數(shù)據(jù)庫測試。實驗分為5個尺度完成測試,在網絡輸入層,圖像分為5幀輸入;當利用3D卷積神經網絡學習到圖像幀的特征后,本文提取網絡最后一層全連接層的特征;最后利用提取到的訓練集特征訓練SVM分類器,從而實現(xiàn)真實人臉和偽裝人臉的分類。實驗結果相對于紋理特征及2D卷積方法有較大提高,可應用于視頻人臉攻擊的活體人臉檢測。
[Abstract]:Illegal authenticators can gain access to face recognition system by camouflage face, which is a threat to social security. Therefore, in vivo face detection has a realistic urgency. However, most of the existing literatures focus on the face attack of photographs. For video face attacks, the recognition rate is not ideal. The Convolutional Neural Network (CNN) is a deep structure, which can automatically learn the distributed feature representation of images. Compared with 2D convolution, we can learn the action information of continuous video frames. Based on the characteristics of 3D convolution neural network, this paper proposes a 3D convolutional neural network for face camouflage detection. The main research contents of this paper are as follows: (1) the existing human face detection algorithms are deeply studied, and the disadvantages of 2DCNN are analyzed, only 2D images can be convoluted; most of the existing live face detection algorithms extract features by hand. The disadvantages are: (1) manual design features only have a better recognition rate for certain types of images; (2) manual design features require higher professional knowledge. In this paper, we firstly apply 3D convolution neural network to face detection in vivo. (2) for two open facial camouflage databases, we design a suitable 3D convolution neural network structure, including the number of layers of the network. Size and number of convolution nuclei. In addition, according to the order in which each network samples in different order in time dimension, three kinds of waiting network (low order early) are designed. Through the experimental test, the recognition rate of each network and the number of network parameters are compared to select the appropriate network. (3) the REPLAY-ATTACK and CASIA-FASD face camouflage database is used as the experimental object. In order to find the optimal input frame number for the classifier, the different frame number input is compared with each other. By extracting the temporal and spatial features learned from the last full connection layer of the network, the support Vector Machine (SVM) classifier is trained to classify real and camouflaged faces. (4) the optimal network and the optimal number of frames are used as inputs. Two open facial camouflage databases, REPLAY-ATTACK and CASIA-FASD, are implemented for multi-scale internal database testing and cross-database testing. The experiment is divided into five scales to complete the test, in the network input layer, the image is divided into five frames input, when using 3D convolution neural network to learn the features of the image frame, this paper extracts the features of the last layer of the network full connection layer. Finally, SVM classifier is trained with the extracted features of training set to realize the classification of real face and camouflage face. Compared with the texture features and 2D convolution, the experimental results can be applied to live face detection of video face attacks.
【學位授予單位】：五邑大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP183

【參考文獻】

相關期刊論文 前2條

1 謝哲;王讓定;嚴迪群;劉華成;;基于同態(tài)補償翻拍圖像的方向預測方法[J];計算機應用;2014年09期

2 曹瑜;涂玲;毋立芳;;身份認證中灰度共生矩陣和小波分析的活體人臉檢測算法[J];信號處理;2014年07期

，

本文編號：2134095