Increasing Trustworthiness of Face Authentication in Mobile Devices by Modeling Gesture Behavior and Location Using Neural Networks

Abstract

Personal mobile devices currently have access to a significant portion of their user's private sensitive data and are increasingly used for processing mobile payments. Consequently, securing access to these mobile devices is a requirement for securing access to the sensitive data and potentially costly services. Face authentication is one of the promising biometrics-based user authentication mechanisms that has been widely available in this era of mobile computing. With a built-in camera capability on smartphones, tablets, and laptops, face authentication provides an attractive alternative of legacy passwords for its memory-less authentication process, which is so sophisticated that it can unlock the device faster than a fingerprint. Nevertheless, face authentication in the context of smartphones has proven to be vulnerable to attacks. In most current implementations, a sufficiently high-resolution face image displayed on another mobile device will be enough to circumvent security measures and bypass the authentication process. In order to prevent such bypass attacks, gesture recognition together with location is proposed to be additionally modeled. Gestures provide a faster and more convenient method of authentication compared to a complex password. The focus of this paper is to build a secure authentication system with face, location and gesture recognition as components. User gestures and location data are a sequence of time series; therefore, in this paper we propose to use unsupervised learning in the long short-term memory recurrent neural network to actively learn to recognize, group and discriminate user gestures and location. Moreover, a clustering-based technique is also implemented for recognizing gestures and location.

Comparative Analysis of Face Detection Using Linear Binary Techniques and Neural Network Approaches

“…Thinking about opposite sides of a coin, every strategy has its solid and frail focuses. The six principle techniques are information -based strategies, appearance-based techniques, include invariant strategies, geometry-based techniques, format based strategies, and model-based techniques [1] The reason for this short audit paper [2] is to introduce, order and assess some new face detection techniques utilizing four ordinary learning machine. The execution and the other assessment parameters of these strategies contrast with one another all together with acquaint critical techniques and furthermore with state favourable circumstances and inconveniences of related works.…”

“…The execution and the other assessment parameters of these strategies contrast with one another all together with acquaint critical techniques and furthermore with state favourable circumstances and inconveniences of related works. [2] The focal point of this paper [3] is to construct a safe confirmation framework with face, area and motion acknowledgment as segments. Client motions and area information are a succession of time arrangement; in this way, in this paper we propose to utilize unsupervised learning in the long transient memory repetitive neural system to effectively figure out how to perceive, gathering and segregate client signals and area.…”

“…Despite the fact that it looks a straightforward arrangement issue, it is perplexing to assemble a decent face classifier. In this manner, learning-based methodologies, for example, neural system based strategies or supports vector machine (SVM) techniques, have been proposed to locate a decent classifiers [2][3][4] [5]. The greater part of proposed calculations use pixel esteems as features.…”

Unveiling the Digital Fingerprints: Analysis of Internet attacks based on website fingerprints

“…A comprehensive review of machine learning supported threat and attack detection is presented in [6,7]. Furthermore, machine learning algorithms could be applied to biometric authentication, as presented by [8], combined with mobile devices as in [9] or to enhance the feature list of standard security tools, such as Burp Suite, as explained by [10].…”

A csökkentett paraméteres biometrikus hitelesítés jelentősége kórházak kritikus informatikai infrastruktúráiban

“…A rendszer az egyedi mintát digitális adattá konvertálja és adatbázisban tárolja. Megkülönböztetjük az első regisztrációs folyamatot, valamint a használat során jelentkező use-case eseteket(Rexha-Shala-Xhafa 2018). Hitelesítés során az aktuálisan levett mintát összevetjük ebben az adatbázisban korábban eltárolt mintákkal.…”