Hand gestures based sign language digits have several contactless applications. Applications include communication for impaired people, such as elderly and disabled people, health-care applications, automotive user interfaces, and security and surveillance. This work presents the design and implementation of a complete end-to-end deep learning based edge computing system that can verify a user contactlessly using ‘authentication code’. The ‘authentication code’ is an ‘n’ digit numeric code and the digits are hand gestures of sign language digits. We propose a memory-efficient deep learning model to classify the hand gestures of the sign language digits. The proposed deep learning model is based on the bottleneck module which is inspired by the deep residual networks. The model achieves classification accuracy of 99.1% on the publicly available sign language digits dataset. The model is deployed on a Raspberry pi 4 Model B edge computing system to serve as an edge device for user verification. The edge computing system consists of two steps, it first takes input from the camera attached to it in real-time and stores it in the buffer. In the second step, the model classifies the digit with the inference rate of 280 ms, by taking the first image in the buffer as input.

https://www.mdpi.com/2079-9292/10/2/182/pdf

Design and Implementation of Deep Learning Based Contactless Authentication System Using Hand Gestures

Face Recognition Smart Attendance System using Deep Transfer Learning

Metaverse describes a new shape of cyberspace and has become a hot-trending word since 2021. There are many explanations about what Meterverse is and attempts to provide a formal standard or definition of Metaverse. However, these definitions could hardly reach universal acceptance. Rather than providing a formal definition of the Metaverse, we list four must-have characteristics of the Metaverse: socialization, immersive interaction, real world-building, and expandability. These characteristics not only carve the Metaverse into a novel and fantastic digital world, but also make it suffer from all security/privacy risks, such as personal information leakage, eavesdropping, unauthorized access, phishing, data injection, broken authentication, insecure design, and more. This paper first introduces the four characteristics, then the current progress and typical applications of the Metaverse are surveyed and categorized into four economic sectors. Based on the four characteristics and the findings of the current progress, the security and privacy issues in the Metaverse are investigated. We then identify and discuss more potential critical security and privacy issues that can be caused by combining the four characteristics. Lastly, the paper also raises some other concerns regarding society and humanity. 

/pdf/security-and-privacy-in-metaverse-a-comprehensive-survey-26695nbz.pdf

Security and Privacy in Metaverse: A Comprehensive Survey

Face detection and recognition is a prevalent concept in security and access control area which is commonly used in surveillance cameras at public places, attendance etc. But often this type of system can be circumvented by holding a photo or running a video of authorized person to the camera. Therefore, liveliness concept comes up with a solution to detect the person is real or spoofed. In this paper, We proposed a cascade classifier based model for detecting liveliness using deep-learning and Heart-beat measurement. Moreover, we have evaluated our model accuracy with our own dataset of real and fake videos and photos. By using our proposed model of face liveliness detection model, FPR and FNR have declined 16% and 5.22% respectively. In addition, we have also compared proposed system with other state-of-art methods. And here proposed study has achieved an accuracy of 99.46%.

Cascade Classification of Face Liveliness Detection Using Heart Beat Measurement

Biometric authentication methods, representing the ”something you are” scheme, are considered the most secure approach for gaining access to protected resources. Recent attacks using Machine Learni...

Attack Analysis of Face Recognition Authentication Systems Using Fast Gradient Sign Method

Face is one of the most widely used biometrics for human identity authentication. Facial recognition has remained an interesting and active research area in the past several decades due to its ever growing applications in biometric authentication, content based data retrieval, video surveillance, access control and social media. Unlike other biometric systems, facial recognition based systems work independently without involving the individual, due to which it does not add unnecessary delay. Its ability of recognizing multiple persons at a time further adds to its speed. There are many face recognition methods based on traditional machine learning that are available in the literature. Improvements are being made with the constant developments in computer vision and machine learning. However, most of the traditional methods lack robustness against varying illumination, facial expression, scale, occlusions and pose. With the advent of big data and graphical computing, deep learning has impressively advanced the traditional computer vision systems over the past decade. In this paper, we present a convolutional neural network based face recognition system which detects faces in an input image using Viola Jones face detector and automatically extracts facial features from detected faces using a pre-trained CNN for recognition. A large database of facial images of subjects is created, which is augmented in order to increase the number of images per subject and to incorporate different illumination and noise conditions for optimal training of the convolutional neural network. Moreover, an optimal pretrained CNN model along with a set of hyperparameters is experimentally selected for deep face recognition. Promising experimental results, with an overall accuracy of 98.76%, are obtained which depict the effectiveness of deep face recognition in automated biometric authentication systems.

Maheen Zulfiqar

Papers

Deep Face Recognition for Biometric Authentication