Daniel Jiménez

Audio-visual speech asynchrony detection using co-inertia analysis and coupled hidden markov models

This paper addresses the subject of liveness detection, which is a test that ensures that biometric cues are acquired from a live person who is actually present at the time of capture. The liveness check is performed by measuring the degree of synchrony between the lips and the voice extracted from a video sequence. Three new methods for asynchrony detection based on co-inertia analysis (CoIA) and a fourth based on coupled hidden Markov models (CHMMs) are derived. Experimental comparisons are made with several methods previously used in the literature for asynchrony detection and speaker location. The reported results demonstrate the effectiveness and superiority of the proposed new methods based on both CoIA and CHMMs as asynchrony detection methods.

An aspect-oriented ambient intelligence middleware platform

Currently, the interest in Ambient Intelligence (or AmI) has increased exponentially due to the widespread use of portable devices. Users demand more and more functionality from these devices, especially in order to perform collaborative tasks and interchange information. As a result, this technology proposes new challenges that must be addressed by both the hardware manufacturers and Software Engineers. The first challenge is to provide a middleware platform providing specific AmI services like communication or device discovery and able to cope with several challenges posed by AmI applications. One of these challenges is to manage heterogeneity of devices that are present in AmI environments in a transparent way, and in particular to manage the evolution of such devices without breaking the code of already developed applications. Moreover, an AmI middleware platform has to support the evolution of the software architecture of AmI applications over time, making it possible to add new functionalities, to adapt applications to any technological changes, and to produce a product line of AmI platforms to be executed in different devices. A final challenge is that the middleware platform has to support the adaptation of AmI applications to unexpected and dynamic changes in AmI environments. Aspect Oriented Programming (AOP) provides good and advanced solutions to the evolution management problem at different levels, so our work focuses on applying AOP to develop an AmI platform. In this paper, we will discuss these problems, propose solutions and present how these problems are handled in our AmI aspect-oriented platform named AOPAmI. This paper describes the internal platform structure and how it provides support to AmI devices. We will focus on the dynamic nature of AmI applications and on how to solve the above mentioned problems.

Multimodal Biometrics-Based Student Attendance Measurement in Learning Management Systems

In this paper we present a solution to obtain useful and reliable user logs in a Learning Management System (LMS).Current LMS logs are combined with biometric-based logs that show the student behaviour. Our system models the student behaviour, allowing to know exactly how much time the student spends in front of the computer examining the con-tents of the LMS. Besides, user verification and face tracking are also integrated, what guarantees that the student is the person actually interacting with the system. The presented multimodal solution for user tracking and user ver-ification combines face tracking, face verification, speakerverification and fingerprint verification. Face tracking and face verification are performed in a non-collaborative fashion. Fingerprint or speaker verification is performed on demand, with the aim of avoiding a negative in¿uence of adverse environmental or behavioural human factors in the reliability of the user logs generated by the system. These circumstances can thwart the non collaborative face veri-fication performance involved in the tracking process. The presented solution solves the problem of user tracking and authentication even in adverse environments for face verifi-cation.In this paper we present a solution to obtain useful and reliable user logs in a Learning Management System (LMS). Current LMS logs are combined with biometric-based logs that show the student behaviour. Our system models the student behaviour, allowing to know exactly how much time the student spends in front of the computer examining the contents of the LMS. Besides, user verification and face tracking are also integrated, what guarantees that the student is the person actually interacting with the system. The presented multimodal solution for user tracking and user verification combines face tracking, face verification, speaker verification and fingerprint verification. Face tracking and face verification are performed in a non-collaborative fashion. Fingerprint or speaker verification is performed on demmand, with the aim of avoiding a negative influence of adverse environmental or behavioural human factors in the reliability of the user logs generated by the system. These circumstances can thwart the non collaborative face verification performance involved in the tracking process. The presented solution solves the problem of user tracking and authentication even in adverse environments for face verification.

Information Retrieval and Text Categorization with Semantic Indexing

In this paper, we present the effect of the semantic indexing using WordNet senses on the Information Retrieval (IR) and Text Categorization (TC) tasks. The documents have been sense-tagged using a Word Sense Disambiguation (WSD) system based on Specialized Hidden Markov Models (SHMMs). The preliminary results showed that a small improvement of the performance was obtained only in the TC task.

Information fusion for local gabor features based frontal face verification

We address the problem of fusion in a facial component approach to face verification. In our study the facial components are local image windows defined on a regular grid covering the face image. Gabor jets computed in each window provide face representation. A fusion architecture is proposed to combine the face verification evidence conveyed by each facial component. A novel modification of the linear discriminant analysis method is presented that improves fusion performance as well as providing a basis for feature selection. The potential of the method is demonstrated in experiments on the XM2VTS data base.

Implementation of an AmI Communication Service Using a Federated Event System Based on Aspects

Event-based communication can be considered naturally suited to support ambient intelligence and ubiquitous computing applications due to its asynchronous nature and due to loose coupling between application components. Event systems support different properties depending on the specific problem domain for which they have been developed. Using these event systems in a federated way, where events are disseminated across the boundaries of a single event system, has been possible in some areas. However, such federation has typically been realized as bilateral inter-working federation between designated pairs of event systems that rely on hardcoded architectures, which are inherently difficult to maintain when systems evolve over time. This paper presents an ambient intelligence platform, called AOPAMI, that uses aspects to enable truly multilateral federation between inter-working heterogeneous event systems. AOPAMI also solves the technology evolution problem using the aspect oriented software development paradigm.

Developing dynamic and adaptable applications with CAM/DAOP: a virtual office application

CAM/DAOP is a component and aspect based model and platform implemented using Java/RMI and reflective techniques. Using CAM/DAOP we have developed several collaborative applications, where the most relevant one is a Virtual Office application, which allows dispersed users to collaborate as if they were co-located. Attendees of the demonstration will see how to develop dynamic and adaptable applications with CAM/DAOP, from the design through to the implementation phases. We will place emphasis on showing how to adapt the behavior of CAM/DAOP applications at runtime, simply by modifying the architectural information provided during the application development.