Presentation Attack Detection in Facial Biometric Authentication

Biometric systems are referred to those structures that enable recognizing an individual, or specifically a characteristic, using biometric data and mathematical algorithms. These are known to be widely employed in various organizations and companies, mostly as authentication systems. Biometric authentic systems are usually much more secure than a classic one, however they also have some loopholes. Presentation attacks indicate those attacks which spoof the biometric systems or sensors. The presentation attacks covered in this project are: photo attacks and deepfake attacks. In the case of photo attacks, it is observed that interactive action check like Eye Blinking proves efficient in detecting liveness. The Convolutional Neural Network (CNN) model trained on the dataset gave 95% accuracy. In the case of deepfake attacks, it is found out that the deepfake videos and photos are generated by complex Generative Adversarial Networks (GANs) and are difficult for human eye to figure out. However, through experiments, it was observed that comprehensive analysis on the frequency domain divulges a lot of vulnerabilities in the GAN generated images. This makes it easier to separate these fake face images from real live faces. The project documents that with frequency analysis, simple linear models as well as complex models give high accuracy results. The models are trained on StyleGAN generated fake images, Flickr-Faces-HQ Dataset and Reface app generated video dataset. Logistic Regression turns out to be the best classifier with test accuracies of 99.67% and 97.96% on two different datasets. Future research can be conducted on different types of presentation attacks like using video, 3-D rendered face mask or advanced GAN generated deepfakes

Application of facial biometric data in recognition of persons

Práce se zabývá tématikou detekce a identifikace lidského obličeje v obraze. Popsány jsou jednotlivé biometrické metody a práce s biometrickými systémy. Dále se práce věnuje problematice zpracování obrazu a realizaci Viola-Jones detektoru při rozpoznávání klíčových bodů v obličeji.he work deals with themes of detection and identification of human faces in an image. Described are the different biometric methods and work with biometric systems. Further, a problem of image processing is described and proposed a method for locating faces in an image and implementation of Viola-Jones detector in identifying key points in the face

A Thematic and Reference Analysis of Touchless Technologies

The purpose of this research is to explore the utility and current state of touchless technologies. Five categories of technologies are identified as a result of collecting and reviewing literature: facial/biometric recognition, gesture recognition, touchless sensing, personal devices, and voice recognition. A thematic analysis was conducted to evaluate the advantages and disadvantages of the five categories. A reference analysis was also conducted to determine the similarities between articles in each category. Touchless sensing showed to have the most advantages and least similar references. Gesture recognition was the opposite. Comparing analyses shows more reliable technology types are more beneficial and diverse

Implementation of K-Nearest Neightbors Face Recognition on Low-power Processor

Face recognition is one of early detection in security system. Automation encourages implementation of face recognition in robot with low-power processor. Most of face recognition research focused on recognition accuration only and performed on high-speed computer. Face recognition that is implemented on low-cost processor, such as ARM processor, needs proper algorithm. Our research proposed K-Nearest Neighbor (KNN) algorithm to recognize face by ARM processor, which was common processor in robot system. This research sought best k-value to create proper face recognition with low-power processor. The proposed algorithm was tested on AT&T face dataset from Computer Laboratory, Cambridge University. The 15 images were set as testing image and 315 images were used as reference data set. OpenCV was choosen as main core image processing library, due to its high-speed. Proposed algorithm was implemented on ARM11 700MHz. Experiment result showed that KNN face recognition detected 93.3% face with k=1. Another proposed Histogram KNN face recognition gave 100% true detection with k=3. Overall experiment showed that proposed algorithm detected face on 2.657 s by ARM processor

Roman portraiture and biometric identification

This project utilised three-dimensional scanning technology in the study of ancient Roman art and archaeology: Roman representations of faces executed in marble. In the cultural heritage sector, three-dimensional (3D) scanning finds its primary application in documenting and reconstructing objects and structures mostly of simple geometry: bones, pottery, architecture or the imprint of whole archaeological sites (Adolf 2011). In forensic science, the face is interesting from investigative and probative perspectives, including both recognition and identification. Biometric methods of facial recognition have been part of a plethora of computer science-based applications used in the verification of identity (Davy et al. 2005, Goodwin, Evison and Schofield 2010). The aim of this initial project is to provide objective relevant measurements of key facial features from the two ancient Roman portrait statue three-dimensional scans, which will allow the delineation of relationships between individual portraits including formal and stylistics aspects. The work described in this paper proposal is truly multidisciplinary, it touches on many fields including : Classical archaeologies (specifically ancient art history in the period of the Roman Empire 31BC - AD400), Forensic Anthropology (specifically physical anthropology and human osteology, Facial Biometrics (specifically uniquely recognising humans based upon their intrinsic physical traits and features) and Computer Science and Statistics (specifically the analysis of large complex multi-dimensional data sets)

Roman portraiture and biometric identification

This project utilised three-dimensional scanning technology in the study of ancient Roman art and archaeology: Roman representations of faces executed in marble. In the cultural heritage sector, three-dimensional (3D) scanning finds its primary application in documenting and reconstructing objects and structures mostly of simple geometry: bones, pottery, architecture or the imprint of whole archaeological sites (Adolf 2011). In forensic science, the face is interesting from investigative and probative perspectives, including both recognition and identification. Biometric methods of facial recognition have been part of a plethora of computer science-based applications used in the verification of identity (Davy et al. 2005, Goodwin, Evison and Schofield 2010). The aim of this initial project is to provide objective relevant measurements of key facial features from the two ancient Roman portrait statue three-dimensional scans, which will allow the delineation of relationships between individual portraits including formal and stylistics aspects. The work described in this paper proposal is truly multidisciplinary, it touches on many fields including : Classical archaeologies (specifically ancient art history in the period of the Roman Empire 31BC - AD400), Forensic Anthropology (specifically physical anthropology and human osteology, Facial Biometrics (specifically uniquely recognising humans based upon their intrinsic physical traits and features) and Computer Science and Statistics (specifically the analysis of large complex multi-dimensional data sets)