Anti-Spoofing of Live Face Authentication on Smartphone

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. W...

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Veröffentlicht in:Journal of Information Science and Engineering 2021-05, Vol.37 (3), p.605-616
Hauptverfasser: 曾子家(TZ-CHIA TSENG), 施登富(TENG-FU SHIH), 傅楸善(CHIOU-SHANN FUH)
Format: Artikel
Sprache:eng
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Zusammenfassung: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, and compare the result of vgg16, InceptionNet, ResNet, DenseNet and MobileNet in CASIA-SURFT dataset.
ISSN:1016-2364
DOI:10.6688/JISE.202105_37(3).0007