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Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security
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作者 Amal H.Alharbi s.karthick +2 位作者 K.Venkatachalam Mohamed Abouhawwash Doaa Sami Khafaga 《Intelligent Automation & Soft Computing》 SCIE 2023年第3期2773-2787,共15页
Recent security applications in mobile technologies and computer sys-tems use face recognition for high-end security.Despite numerous security tech-niques,face recognition is considered a high-security control.Develop... Recent security applications in mobile technologies and computer sys-tems use face recognition for high-end security.Despite numerous security tech-niques,face recognition is considered a high-security control.Developers fuse and carry out face identification as an access authority into these applications.Still,face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user.In the existing spoofing detection algorithm,there was some loss in the recreation of images.This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame of the sequenced set of frames to overcome the above-said problems.This research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused structure.First,this pro-posed method is tested with the Cross-ethnicity Face Anti-spoofing(CASIA),Fetal alcohol spectrum disorders(FASD)dataset.This database has three models of attacks:distorted photographs in printed form,photographs with removed eyes portion,and video attacks.The images are taken with three different quality cameras:low,average,and high-quality real and spoofed images.An extensive experimental study was performed with CASIA-FASD,3 Diagnostic Machine Aid-Digital(DMAD)dataset that proved higher results when compared to existing algorithms. 展开更多
关键词 Image processing edge detection edge net auto-encoder face authentication digital security
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