Anti-spoofing of live face authentication on smartphone
Journal
Journal of Information Science and Engineering
Journal Volume
37
Journal Issue
3
Pages
605-616
Date Issued
2021
Author(s)
Abstract
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. Finally, we compared to Parkin and Grinchuk's results, using dataset CASIA-SURF [1], and compare the result of vgg16, InceptionNet, ResNet, DenseNet and MobileNet in CASIA-SURFT dataset. ? 2021 Institute of Information Science. All rights reserved.
Subjects
Cameras; Data streams; Photography; Anti-spoofing; Convolution neural network; Data augmentation; Digital photographs; Face authentication; Face classifiers; Spoofing attacks; Thermal sensors; Smartphones
Type
journal article