A human computer interactions framework for biometric user identification

Computer assisted functionalities and services have saturated our world becoming such an integral part of our daily activities that we hardly notice them. In this study we are focusing on enhancements in Human-Computer Interaction (HCI) that can be achieved by natural user recognition embedded in the employed interaction models. Natural identification among humans is mostly based on biometric characteristics representing what-we-are (face, body outlook, voice, etc.) and how-we-behave (gait, gestures, posture, etc.) Following this observation, we investigate different approaches and methods for adapting existing biometric identification methods and technologies to the needs of evolving natural human computer interfaces

On Using Gait in Forensic Biometrics

Given the continuing advances in gait biometrics, it appears prudent to investigate the translation of these techniques for forensic use. We address the question as to the confidence that might be given between any two such measurements. We use the locations of ankle, knee and hip to derive a measure of the match between walking subjects in image sequences. The Instantaneous Posture Match algorithm, using Harr templates, kinematics and anthropomorphic knowledge is used to determine their location. This is demonstrated using real CCTV recorded at Gatwick Airport, laboratory images from the multi-view CASIA-B dataset and an example of real scene of crime video. To access the measurement confidence we study the mean intra- and inter-match scores as a function of database size. These measures converge to constant and separate values, indicating that the match measure derived from individual comparisons is considerably smaller than the average match measure from a population

Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation

Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions

Lateral Touch and Frictional Vibrations of Human Fingerprints

In this report, we experimentally show that human fingerprints play a significant role in the lateral touch vibratory mechanisms and that direction of movement results in differ-ent dynamic strains on the pulp. Sources that generate sound at the interface are first empiri-cally identified and a spherical model of the index finger is proposed to forecast the radiated fields. Then, haptic sounds are recorded using a miniature microphone placed in the near field. Results are compared with vibrometric measurements carried out in quite similar conditions to assess of the results relevance. Results show that fingerprints can?t be neglected in the tri-bologic interaction. Finally, this research provide a useful information for tactile displays de-signers and offers future investigations perspectives in many research fields linked with the haptic science

DragID: A Gesture Based Authentication System

Department of Electrical EngineeringWith the use of mobile computing devices with touch screens is becoming widespread. Sensitive personal information is often stored in the mobile devices. Smart device users use applications with sensitive personal data such as in online banking. To protect personal information, code based screen unlock methods are used so far. However, these methods are vulnerable to shoulder surfing or smudge attacks. To build a secure unlocking methods we propose DragID, a flexible gesture and biometric based user authentication. Based on the human modeling, DragID authenticates users by using 6 input sources of touch screens. From the input sources, we build 25 fine grained features such as origin of hand, finger radius, velocity, gravity, perpendicular and so on. As modeling the human hand, inour method, features such as radius or origin is difficult to imitate. These features are useful for authentication. In order to authenticate, we use a popular machine learning method, support vector machine. This method prevents attackers reproducing the exact same drag patterns. In the experiments, we implemented DragID on Samsung Galaxy Note2, collected 147379 drag samples from 17 volunteers, and conducted real-world experiments. Our method outperforms Luca???s method and achieves 89.49% and 0.36% of true positive and false positive. In addition, we achieve 92.33% of TPR in case we implement sequence technique.ope