Recognition of Biometric Unlock Pattern by GMM-UBM

International audienceUnlock patterns are used for authentication in mobile smart devices, yet they are vulnerable to attacks, since only the pattern draw is required. Extra biometric data of the user while drawing the unlock pattern passwords may strengthen the authentication, such as the speed of drawing, the pressure of the finger on the touch screen. Such biometric modality is referred to as behavioral biometrics. Besides, voice is also a behavioral biometric modality, as well as a physiological one. Hence, statistical models such as Gaussian mixture models (GMM) with universal background modeling (UBM) are widely used in speaker verification systems. In this work, we propose to apply and adapt a framework usually dedicated to speaker verification to recognize the unlock patterns based on users' behavior. We evaluate the performance using equal error rate for different combinations of features and varying number of mixtures. As a result of the combination of features, an equal error rate as low as 9.25% on average is obtained, which is promising for a preliminary study on GMM-UBM applied to unlock pattern based biometric recognition

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author