智慧型手機真人臉型驗證

Abstract

本論文提出一個在不藉助深度相機鏡頭、紅外線熱感應鏡頭等額外裝置，僅使用智慧型手機單顆可見光前鏡頭，辨識取得的影像來自真人使用者，或電子欺騙攻擊 (Spoofing Attack)，包含紙本相片、電子相片、影片等。首先我們透過單樣本多框偵測器 (Single Shot Multi-Box Detector, SSD) 擷取畫面中的人臉，經過特定的資料擴增 (Data Augmentation) 後，再餵入捲積類神經網路 (Convolution Neural Network, CNN) ，訓練出具有分辨真人使用者與電子欺騙攻擊的模型。

Our proposed method is capable of authenticating the input image is from real user or spoofing attack, including paper photograph, digital photograph, and video, using only the Red, Green, Blue (RGB) frontal camera of common smart phone, without the help of depth camera or infrared thermal sensor. We first capture live faces in each frame of input video streams by single shot multi-box detector then feed into our designed convolution neural network after certain data augmentation and finally obtain a well-trained spoof face classifier.