Soumik Mondal

A computational approach to the continuous authentication biometric system

In this paper, we investigate the performance of a continuous biometric authentication system under various different analysis techniques. We test these on a publicly available continuous mouse dynamics database, but the techniques can be applied to other biometric modalities in a continuous setting also. We test all different combinations of fusion techniques, threshold settings, score boosting techniques and static versus dynamic trust models. We extensively describe the way that performance is reported when analyzing the performance of a continuous authentication system. Contrary to a biometric system for access control at the start of a session can the performance not simply be reported by a single EER value or a DET curve. We show that the optimal performance we can reach with our new techniques improves significantly over the best known performance on the same dataset.

Performance evaluation of continuous authentication systems

In this study, the authors will describe how performance results for continuous authentication (CA) should be reported. Most research on alleged CA is in fact periodic authentication, and performance is then reported in false match and false non-match rates. Here the authors will describe average number of impostor or genuine actions as the performance indicators, and will describe a more detailed performance reporting method. The authors’ current results have been reported in continuous authentication, based on analysis performed on two different datasets, and compared those results to the best results in comparable research, where they show that their results outperform most other known results.

A study on continuous authentication using a combination of keystroke and mouse biometrics

In this paper we focus on a context independent continuous authentication system that reacts on every separate action performed by a user. We contribute with a robust dynamic trust model algorithm that can be applied to any continuous authentication system, irrespective of the biometric modality. We also contribute a novel performance reporting technique for continuous authentication. Our proposed approach was validated with extensive experiments with a unique behavioural biometric dataset. This dataset was collected under complete uncontrolled condition from 53 users by using our data collection software. We considered both keystroke and mouse usage behaviour patterns to prevent a situation where an attacker avoids detection by restricting to one input device because the system only checks the other input device. During our research, we developed a feature selection technique that could be applied to other pattern recognition problems.The best result obtained in this research is that 50 out of 53 genuine users are never inadvertently locked out by the system, while the remaining 3 genuine users (i. e. 5.7%) are sometimes locked out, on average after 2265 actions. Furthermore, there are only 3 out of 2756 impostors not been detected, i.e. only 0.1% of the impostors go undetected. Impostors are detected on average after 252 actions.

One-handed Keystroke Biometric Identification Competition

This work presents the results of the One-handed Keystroke Biometric Identification Competition (OhKBIC), an official competition of the 8th IAPR International Conference on Biometrics (ICB). A unique keystroke biometric dataset was collected that includes freely-typed long-text samples from 64 subjects. Samples were collected to simulate normal typing behavior and the severe handicap of only being able to type with one hand. Competition participants designed classification models trained on the normally-typed samples in an attempt to classify an unlabeled dataset that consists of normally-typed and one-handed samples. Participants competed against each other to obtain the highest classification accuracies and submitted classification results through an online system similar to Kaggle. The classification results and top performing strategies are described.

Swipe gesture based Continuous Authentication for mobile devices

In this research, we investigated the performance of a continuous biometric authentication system for mobile devices under various different analysis techniques. We tested these on a publicly available swipe gestures database with 71 users, but the techniques can also be applied to other biometric modalities in a continuous setting. The best result obtained in this research is that (1) none of the 71 genuine users is lockout from the system; (2) for 68 users we require on average 4 swipe gestures to detect an imposter; (3) for the remaining 3 genuine users, on average 14 swipes are required while 4 impostors are not detected.

Continuous Authentication in a real world settings

Continuous Authentication by analysing the users behaviour profile on the computer input devices is challenging due to limited information, variability of data and the sparse nature of the information. As a result, most of the previous research was done as a periodic authentication, where the analysis was made based on a fixed number of actions or fixed time period. Also, the experimental data was obtained for most of the previous research in a very controlled condition, where the task and environment were fixed. In this paper, we will focus on actual continuous authentication that reacts on every single action performed by the user. The experimental data was collected in a complete uncontrolled condition from 52 users by using our data collection software. In our analysis, we have considered both keystroke and mouse usages behaviour pattern to avoid a situation where an attacker avoids detection by restricting to one input device because the continuous authentication system only checks the other input device. The result we have obtained from this research is satisfactory enough for further investigation on this domain.

Continuous Authentication using Fuzzy Logic

In this paper, we investigate the performance of a continuous authentication system using fuzzy logic techniques. The system monitors mouse and keystroke dynamics behaviour of a user to determine the genuineness. The objective was to design a system that is capable of detecting an impostor user as fast as possible, while not disturbing the genuine user. For our research we build a new dataset consisting of mouse and keystroke dynamics behavioural data of 52 persons, collected in a real life environment (no control over environment or performed tasks) over a period of 5-7 days. Using fuzzy logic techniques we found that the objectives were not completely met, meaning that some impostors were not detected by the system and that some genuine users were locked out by the system, but the results are satisfactory enough to serve as a good starting point for this research.

Combining keystroke and mouse dynamics for continuous user authentication and identification

In this paper, we analyze the performance of a continuous user authentication and identification system for a PC under various analysis techniques. We applied a novel identification technique called Pairwise User Coupling (PUC) on our own dataset for the analysis. This dataset is a combination of keystroke and mouse usage behaviour data. We obtained an identification accuracy of 62.2% for a closed-set experiment, where the system needs on average of 471 actions to detect an impostor. In case of an open-set experiment the Detection and Identification Rate (DIR) of 58.9% was obtained, where the system needs on average of 333 actions to detect an impostor.

An Empirical Study of Smartphone Based Iris Recognition in Visible Spectrum

The advanced technologies and sensors in smartphones has led to showcase their potential as a biometric sensor. In this work, we present the feasibility study and challenges in the path forward for using smartphone as a biometric sensor for iris recognition in visible spectrum. Especially, with a limited shelf-life of smartphones, it is anticipated to have enrolment and verification using different camera. In this work, we propose an improvement to segmentation scheme for contactless iris acquisition by approximating the radius range. The proposed method has resulted in a segmentation accuracy of 81%. We also propose various protocols for real-life verification scenarios using smartphones for visible spectrum iris recognition. Finally, results from an extensive set of experiments are presented to validate the anticipated challenges in using smartphone based iris recognition. Being the first of its kind, this work provides the benchmarking results for the smartphone iris database. The best EER is obtained for iPhone in indoor scenario with an impressive EER of 0.48%.

Keystroke dynamics performance enhancement with soft biometrics

It is accepted that the way a person types on a keyboard contains timing patterns, which can be used to classify him/her, is known as keystroke dynamics. Keystroke dynamics is a behavioural biometric modality, whose performances, however, are worse than morphological modalities such as fingerprint, iris recognition or face recognition. To cope with this, we propose to combine keystroke dynamics with soft biometrics. Soft biometrics refers to biometric characteristics that are not sufficient to authenticate a user (e.g. height, gender, skin/eye/hair colour). Concerning keystroke dynamics, three soft categories are considered: gender, age and handedness. We present different methods to combine the results of a classical keystroke dynamics system with such soft criteria. By applying simple sum and multiply rules, our experiments suggest that the combination approach performs better than the classification approach with best result of 5.41% of equal error rate. The efficiency of our approaches is illustrated on a public database.