本文選題：D卷積神經網絡 + 活體人臉檢測��； 參考：《信號處理》2017年11期

【摘要】：非法入侵者通過偽裝人臉騙取系統(tǒng)認證,給人臉認證系統(tǒng)帶來了嚴重的威脅。因此,活體人臉檢測成了人臉認證系統(tǒng)走向實用必須解決的一個重要課題。現有活體人臉檢測方法多為基于照片的人臉攻擊方面的研究成果,對于基于視頻的人臉攻擊,效果并不理想。3D卷積神經網絡(Convolutional Neural Network,CNN)具有深度學習的特點,能自動學到圖像的分布式特征表示;與2D卷積相比,它能學到連續(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卷積實現視頻人臉偽裝檢測。通過提取3D卷積神經網絡最后全連接層學到的時間空間特征,訓練SVM(Support Vector Machine)分類器,實現真實人臉和偽裝人臉的分類。實驗采用兩個人臉偽裝公開數據庫Replay Attack和CASIA,實現多尺度內部數據庫測試和交叉數據庫測試。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 intruders deceptive system authentication by camouflage face, bring serious threat to face authentication system. Therefore, face detection in vivo has become an important issue that must be solved in the face authentication system. Most of the existing face detection methods are based on photos. For video-based face attacks, the effect is not ideal. 3D convolutional neural network (CNN) has the characteristics of deep learning. It can automatically learn the distributed feature representation of images, and it can learn the action information of continuous video frames compared with 2D convolution. Based on the characteristics of 3D convolution neural network, a video face camouflage detection based on 3D convolution is proposed in this paper. By extracting the temporal and spatial features of 3D convolutional neural network and training SVM support Vector Machine, the classification of real face and camouflage face is realized. Two human face camouflaged open databases, replay Attack and CASIA, are used to realize multi-scale internal database testing and cross-database testing. Compared with the texture features and 2D convolution, the experimental results can be applied to live face detection of video face attacks.
【作者單位】： 五邑大學信息工程學院;
【基金】：國家自然科學基金項目(61372193,61070167) 廣東高校優(yōu)秀青年教師培訓計劃資助項目(SYQ2014001) 廣東省特色創(chuàng)新項目(2015KTSCX143,2015KTSCX145) 廣東省青年創(chuàng)新項目(2016KQNCX171)
【分類號】：TP183;TP391.41
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本文編號：2012074