Enrique Argones Rúa

Reliability-Informed Beat Tracking of Musical Signals

A new probabilistic framework for beat tracking of musical audio is presented. The method estimates the time between consecutive beat events and exploits both beat and non-beat information by explicitly modeling non-beat states. In addition to the beat times, a measure of the expected accuracy of the estimated beats is provided. The quality of the observations used for beat tracking is measured and the reliability of the beats is automatically calculated. A k -nearest neighbor regression algorithm is proposed to predict the accuracy of the beat estimates. The performance of the beat tracking system is statistically evaluated using a database of 222 musical signals of various genres. We show that modeling non-beat states leads to a significant increase in performance. In addition, a large experiment where the parameters of the model are automatically learned has been completed. Results show that simple approximations for the parameters of the model can be used. Furthermore, the performance of the system is compared with existing algorithms. Finally, a new perspective for beat tracking evaluation is presented. We show how reliability information can be successfully used to increase the mean performance of the proposed algorithm and discuss how far automatic beat tracking is from human tapping.

An Evaluation of Video-to-Video Face Verification

Person recognition using facial features, e.g., mug-shot images, has long been used in identity documents. However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realize facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. This paper presents an evaluation of person identity verification using facial video data, organized in conjunction with the International Conference on Biometrics (ICB 2009). It involves 18 systems submitted by seven academic institutes. These systems provide for a diverse set of assumptions, including feature representation and preprocessing variations, allowing us to assess the effect of adverse conditions, usage of quality information, query selection, and template construction for video-to-video face authentication.

Biometric Template Protection Using Universal Background Models: An Application to Online Signature

Data security and privacy are crucial issues to be addressed for assuring a successful deployment of biometrics-based recognition systems in real life applications. In this paper, a template protection scheme exploiting the properties of universal background models, eigen-user spaces, and the fuzzy commitment cryptographic protocol is presented. A detailed discussion on the security and information leakage of the proposed template protection system is given. The effectiveness of the proposed approach is investigated with application to online signature recognition. The given experimental results, evaluated on the public MCYT signature database, show that the proposed system can guarantee competitive recognition accuracy while providing protection to the employed biometric data.

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.

Quality-Based Score Normalization and Frame Selection for Video-Based Person Authentication

This paper addresses the incorporation of quality measures to video-based person authentication. A theoretical framework to incorporate quality measures in biometric authentication is exposed. Two different quality-based score normalization techniques are derived from this theoretical framework. Furthermore, a quality-based frame selection technique and a new face image quality measure are also presented. The ability of this quality measure and the proposed quality-based score normalization techniques and quality-based frame selection technique to improve verification performance is experimentally evaluated in a video-based face verification experiment on the BANCA Database.This paper addresses the incorporation of quality measures to videobased person authentication. A theoretical framework to incorporate quality measures in biometric authentication is exposed. Two different quality-based score normalization techniques are derived from this theoretical framework. Furthermore, a quality-based frame selection technique and a new face image quality measure are also presented. The ability of this quality measure and the proposed quality-based score normalization techniques and quality-based frame selection technique to improve verification performance is experimentally evaluated in a video-based face verification experiment on the BANCA Database.

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.

Face Video Competition

Person recognition using facial features, e.g., mug-shot images, has long been used in identity documents. However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realise facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. This paper presents the first known benchmarking effort of person identity verification using facial video data. The evaluation involves 18 systems submitted by seven academic institutes.

Ergodic HMM-UBM system for on-line signature verification

We propose a novel approach for on-line signature verification based on building HMM user models by adapting an ergodic Universal Background Model (UBM). State initialization of this UBM is driven by a dynamic signature feature. This approach inherits the properties of the GMM-UBM mechanism, such as minimizing overfitting due to scarcity of user training data and allowing a world-model type of likelihood normalization. This system is experimentally compared to a baseline state-of-the-art HMM-based online signature verification system using two different databases: the well known MCYT-100 corpus and a subset of the signature part of the BIOSECURE-DS2 corpus. The HMM-UBM approach obtains promising results, outperforming the baseline HMM-based system on all the experiments.

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.

DEMO: Gradiant asymmetric encryption and verification systems based on handwritten signature

A successful deployment of biometric-based recognition systems in real-life applications depends on crucial issues such as data security and privacy, which have to be specifically addressed. Besides, cryptographic key protection can represent the main weakness of a secured transmission. In this demonstration a system for encryption and digital signature of generic digital documents (SAES, standing for Signature-based Assymetric Encryption System) is presented, where cryptographic keys are protected by the hand-written signature of the user. Furthermore, a demonstration of a the handwritten online signature verification system (SVS) based on non-protected templates will also be performed.