Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

Overcoming Inter-Subject Variability in BCI Using EEG-Based Identification

The high dependency of the Brain Computer Interface (BCI) system performance on the BCI user is a well-known issue of many BCI devices. This contribution presents a new way to overcome this problem using a synergy between a BCI device and an EEG-based biometric algorithm. Using the biometric algorithm, the BCI device automatically identifies its current user and adapts parameters of the classification process and of the BCI protocol to maximize the BCI performance. In addition to this we present an algorithm for EEG-based identification designed to be resistant to variations in EEG recordings between sessions, which is also demonstrated by an experiment with an EEG database containing two sessions recorded one year apart. Further, our algorithm is designed to be compatible with our movement-related BCI device and the evaluation of the algorithm performance took place under conditions of a standard BCI experiment. Estimation of the mu rhythm fundamental frequency using the Frequency Zooming AR modeling is used for EEG feature extraction followed by a classifier based on the regularized Mahalanobis distance. An average subject identification score of 96 % is achieved

A Survey on Ear Biometrics

Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers

A methodology for software performance modeling and its application to a border inspection system

It is essential that software systems meet their performance objectives. Many factors affect software performance and it is fundamental to identify those factors and the magnitude of their effects early in the software lifecycle to avoid costly and extensive changes to software design, implementation, or requirements. In the last decade the development of techniques and methodologies to carry out performance analysis in the early stages of the software lifecycle has gained a lot of attention within the research community. Different approaches to evaluate software performance have been developed. Each of them is characterized by a certain software specification and performance modeling notation.;In this thesis we present a methodology for predictive performance modeling and analysis of software systems. We use the Unified Modeling Language (UML) as a software modeling notation and Layered Queuing Networks (LQN) as a performance modeling notation. Our focus is on the definition of a UML to LQN transformation We extend existing approaches by applying the transformation to a different set of UML diagrams, and propose a few extensions to the current UML Profile for Schedulability, Performance, and Time , which we use to annotate UML diagrams with performance-related information. We test the applicability of our methodology to the performance evaluation of a complex software system used at border entry ports to grant or deny access to incoming travelers