Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

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.

Challenge-Response Emotion Authentication Algorithm Using Modified Horizontal Deep Learning

Face authentication is an important biometric authentication method commonly used in security applications.It is vulnerable to different types of attacks that use authorized users’facial images and videos captured from social media to perform spoofing attacks and dynamic movements for penetrating secur-ity applications.This paper presents an innovative challenge-response emotions authentication model based on the horizontal ensemble technique.The proposed model provides high accurate face authentication process by challenging the authorized user using a random sequence of emotions to provide a specific response for every authentication trial with a different sequence of emotions.The proposed model is applied to the KDEF dataset using 10-fold cross-valida-tions.Several improvements are made to the proposed model.First,the VGG16 model is applied to the seven common emotions.Second,the system usability is enhanced by analyzing and selecting only the four common and easy-to-use emotions.Third,the horizontal ensemble technique is applied to enhance the emotion recognition accuracy and minimize the error during authen-tication processes.Finally,the Horizontal Ensemble Best N-Losses(HEBNL)is applied using challenge-response emotion to improve the authentication effi-ciency and minimize the computational power.The successive improvements implemented on the proposed model led to an improvement in the accuracy from 92.1%to 99.27%.