Just Gaze and Wave: Exploring the Use of Gaze and Gestures for Shoulder-surfing Resilient Authentication

Eye-gaze and mid-air gestures are promising for resisting various types of side-channel attacks during authentication. However, to date, a comparison of the different authentication modalities is missing. We investigate multiple authentication mechanisms that leverage gestures, eye gaze, and a multimodal combination of them and study their resilience to shoulder surfing. To this end, we report on our implementation of three schemes and results from usability and security evaluations where we also experimented with fixed and randomized layouts. We found that the gaze-based approach outperforms the other schemes in terms of input time, error rate, perceived workload, and resistance to observation attacks, and that randomizing the layout does not improve observation resistance enough to warrant the reduced usability. Our work further underlines the significance of replicating previous eye tracking studies using today's sensors as we show significant improvement over similar previously introduced gaze-based authentication systems

A contactless identification system based on hand shape features

This paper aims at studying the viability of setting up a contactless identification system based on hand features, with the objective of integrating this functionality as part of different services for smart spaces. The final identification solution will rely on a commercial 3D sensor (i.e. Leap Motion) for palm feature capture. To evaluate the significance of different hand features and the performance of different classification algorithms, 21 users have contributed to build a testing dataset. For each user, the morphology of each of his/her hands is gathered from 52 features, which include bones length and width, palm characteristics and relative distance relationships among fingers, palm center and wrist. In order to get consistent samples and guarantee the best performance for the device, the data collection system includes sweet spot control; this functionality guides the users to place the hand in the best position and orientation with respect to the device. The selected classification strategies - nearest neighbor, supported vector machine, multilayer perceptron, logistic regression and tree algorithms - have been evaluated through available Weka implementations. We have found that relative distances sketching the hand pose are more significant than pure morphological features. On this feature set, the highest correct classified instances (CCI) rate (>96%) is reached through the multilayer perceptron algorithm, although all the evaluated classifiers provide a CCI rate above 90%. Results also show how these algorithms perform when the number of users in the database change and their sensitivity to the number of training samples. Among the considered algorithms, there are different alternatives that are accurate enough for non-critical, immediate response applications

Contactless User Authentification Using Leap Motion Sensor

Bezkontaktná autentizácia užívateľov naberá ako nová technológia na popularite. Poznatky v spracovaní obrazu a ich aplikácia prispeli k vzniku zariadení akým je ovládač Leap Motion. Toto zariadenie od firmy s rovnakým názvom Leap Motion inc. dokáže určovať polohu ruky v priestore a rozpoznávať jednoduché gestá. Táto práca sa zaoberá aplikáciou dát z rozhrania tohto ovládača pri identifikácii osôb. Využité sú už overené charakteristiky geometrie ruky, ako šírky a dĺžky prstov. Cieľ práce je najmä overiť použitie tohto na trhu dostupného a cenovo nenáročného senzoru pri rozpoznávaní. Užívateľ si toto zariadenie pravdepodobne nezakúpi kvôli jeho vlastnosti autentifikácie osôb, no môže využívať túto jednoduchú formu zabezpečenia bez akejkoľvek ďalšej investície. Nakoniec, výsledky práce ukazujú presnosť okolo 99 % na menšej vzorke ľudí, ktorá pripomína práve domáce použitie.Contactless authentication of users has grown in popularity as a new technology. Recent findings in the field of computer vision and its applications have contributed to the emergence of new devices such as Leap Motion controller. This device is capable of precise recognition of hand positions and simple gestures identification. This paper presents an application of data gathered from controller's interface and using them for user identification. Proposed solution uses hand geometry to evaluate the recognition process where this accessible and inexpensive device can be used. Therefore, the user can easily benefit from this extra feature coming with the new device. To conclude, the results show overall accuracy over 99 % on a relatively small dataset.

Soft biometrics through hand gestures driven by visual stimuli

We present a novel biometric solution which exploits hand gestures, tracked by the Microsoft Kinect sensor, performed in response to a circle randomly appearing in five predefined screen positions. Features of both hand and screen pointer are used for classification purposes, considering both the whole 20-path trajectory and shorter routes. In particular, we search for the "optimal" trajectory length which assures a good trade-off between precision and user effort. For identification, the approach achieves classification accuracies ranging from 0.748 to 0.942. For verification, accuracy is still satisfactory (always higher than 0.962), despite moderate specificity values. Keywords: Soft biometrics, Gestures, Visual stimul