A Leap Password based verification system

Abstract: Modern hardware and software development has led to an evolution of user interfaces from command-line to natural user interfaces for virtual immersive environments. Gestures imitating real-world interaction tasks increasingly replace classical two-dimensional interfaces based on Windows/Icons/Menus/Pointers (WIMP) or touch metaphors. Thus, the purpose of this paper is to survey the state-of-the-art Human-Computer Interaction (HCI) techniques with a focus on the special field of three-dimensional interaction. This includes an overview of currently available interaction devices, their applications of usage and underlying methods for gesture design and recognition. Focus is on interfaces based on the Leap Motion Controller (LMC) and corresponding methods of gesture design and recognition. Further, a review of evaluation methods for the proposed natural user interfaces is given.

Abstract: In this paper, the applicability of hand tremor-based biometric recognition via leap motion device is investigated. The hypothesis is that the hand tremor is unique for humans and can be utilized as a biometric identification. In order to verify our hypothesis, spatiotemporal hand tremor signals are acquired from subjects. The objective is to establish a live and secure identification system to avoid mimic and cloning of password by attackers. Various feature extraction methods, including statistical, fast Fourier transform, discrete wavelet transform, and 1-D local binary pattern are used. For evaluating recognition performance, Naive Bayes and Multi-Layer Perceptron are utilized as linear-simple and nonlinear-complex classifiers, respectively. Since the conducted experiments produced promising results (above 95% of classification accuracy rate), it is considered that the proposed approach has the potential to be used as a new biometric identification manner in the field of security.

Abstract: The gesture-based human-computer interface requires new user authentication technique because it does not have traditional input devices like keyboard and mouse In this paper, we propose a new finger-gesture-based authentication method, where the in-air-handwriting of each user is captured by wearable inertial sensors Our approach is featured with the utilization of both the content and the writing convention, which are proven to be essential for the user identification problem by the experiments A support vector machine (SVM) classifier is built based on the features extracted from the hand motion signals To quantitatively benchmark the proposed framework, we build a prototype system with a custom data glove device The experiment result shows our system achieve a 01% equal error rate (EER) on a dataset containing 200 accounts that are created by 116 users Compared to the existing gesture-based biometric authentication systems, the proposed method delivers a significant performance improvement

Abstract: Signature recognition is identifying the signature’s owner, whereas verification is the process to find whether a signature is genuine or forged. Though, both are important in the field of forensic sciences, however, verification is more important to banks and credit card companies. In this paper, we have proposed a methodology to analyze 3D signatures captured using Leap motion sensor. We have extended existing 2D features into 3D from raw signatures and applied well-known classifiers for recognition as well as verification. We have shown that the 3rd dimension, which essentially represents instantaneous pressure during writing, can improve the accuracy of the biometric systems. We have created a large dataset containing more than 2000 signatures registered by 100 volunteers using the Leap motion interface. This has been made available online for the research community. Our analysis shows that, the proposed 3D extension is better than its original 2D version. Recognition and verification accuracy have increased by 6.8% and 9.5%, respectively using k-NN. Similarly, accuracy has increased by 9.9% (recognition) and 6.5% (verification) when HMM is used as the classifier. Similar results have been recorded on benchmark datasets. A comparison with 2D tablet-stylus interface has been carried out which also supports our claims. We believe, Leap motion can be an alternative to the existing biometric setups.

Abstract: On wearable and Virtual Reality (VR) platforms, user authentication is a basic function, but usually a keyboard or touchscreen cannot be provided to type a password. Hand gesture and especially in-air-handwriting can be potentially used for user authentication because a gesture input interface is readily available on these platforms. However, determining whether a login request is from the legitimate user based on a piece of hand movement is challenging in both signal processing and matching, which leads to limited performance in existing systems. In this paper, we propose a multifactor user authentication framework using both the motion signal of a piece of in-air-handwriting and the geometry of hand skeleton captured by a depth camera. To demonstrate this framework, we invented a signal matching algorithm, implemented a prototype, and conducted experiments on a dataset of 100 users collected by us. Our system achieves 0.6% Equal Error Rate (EER) without spoofing attack and 3.4% EER with spoofing only data, which is a significant improvement compared to existing systems using the Dynamic Time Warping (DTW) algorithm. In addition, we presented an in-depth analysis of the utilized features to explain the reason for the performance boost.

Abstract: Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.

Abstract: Two formulas are presented for judging the significance of the difference between correlated proportions. The chi square equivalent of one of the developed formulas is pointed out.

Abstract: Recent advances in 3D depth cameras such as Microsoft Kinect sensors (www.xbox.com/en-US/kinect) have created many opportunities for multimedia computing. The Kinect sensor lets the computer directly sense the third dimension (depth) of the players and the environment. It also understands when users talk, knows who they are when they walk up to it, and can interpret their movements and translate them into a format that developers can use to build new experiences. While the Kinect sensor incorporates several advanced sensing hardware, this article focuses on the vision aspect of the Kinect sensor and its impact beyond the gaming industry.