MPBS 2013 Abstracts


Full Papers
Paper Nr: 1
Title:

Expression Detector System based on Facial Images

Authors:

José G. Hernández-Travieso, Carlos M. Travieso, Marcos del Pozo-Baños and Jesús B. Alonso

Abstract: This paper proposes a emotion detector, applied for facial images, based on the analysis of facial segmentation. The parameterizations have been developed on spatial and transform domains, and the classification has been done by Support Vector Machines. A public database has been used in experiments, The Radboud Faces Database (RAFD), with eight possible emotions: anger, disgust, fear, happiness, sadness, surprise, neutral and contempt. Our best approach has been reached with decision fusion, using transform domains, reaching an accurate up to 96.62%.

Paper Nr: 2
Title:

On the Repeatability of EEG Features in a Biometric Recognition Framework using a Resting State Protocol

Authors:

Daria La Rocca, Patrizio Campisi and Gaetano Scarano

Abstract: In this paper the feasibility of the electroencephalogram (EEG) as biometric identifier is investigated with focus on the repeatability of the EEG features employed in the proposed framework. The use of EEG within the biometric framework has already been introduced in the recent past although it has not been extensively analyzed. In this contribution we infer about the invariance over time of the employed EEG features, which is one of the most relevant properties a biometric identifier should possess in order to be employed in real life applications. For the purpose of this study we rely on the “resting state” protocol. The employed database is composed by healthy subjects whose EEG signals have been acquired in two different sessions. Different electrodes configurations pertinent to the employed protocol have been considered. Autoregressive statistical modeling using reflection coefficients has been adopted and a linear classifier has been tested. The obtained results show that a high degree of repeatability has been achieved over the considered interval.

Paper Nr: 3
Title:

Multi-scale, Multi-feature Vector Flow Active Contours for Automatic Multiple Face Detection

Authors:

Joanna Isabelle Olszewska

Abstract: To automatically detect faces in real-world images presenting challenges such as complex background and multiple foregrounds, we propose a new method which is based on parametric active contours and which does not require any supervision, model nor training. The proposed face detection technique computes multi-scale representations of an input color image and based on them initializes the multi-feature vector flow active contours which, after their evolution, automatically delineate the faces. In this way, our computationally efficient system successfully detects faces in complex pictures with varying numbers of persons of diverse gender and origins and with different types of face views (front/profile) and variate face alignments (straight/oblique), as demonstrated in tests carried out on several datasets.

Short Papers
Paper Nr: 5
Title:

Parameterization of Written Signatures based on EFD

Authors:

Pere Marti-Puig, Jaume Danés and Jordi Solé-Casals

Abstract: In this work we propose a method to quantify written signatures from digitalized images based on the use of Elliptical Fourier Descriptors (EFD). As usually signatures are not represented as a closed contour, and being that a necessary condition in order to apply EFD, we have developed a method that represents the signatures by means of a set of closed contours. One of the advantages of this method is that it can reconstruct the original shape from all the coefficients, or an approximated shape from a reduced set of them finding the appropriate number of EFD coefficients required for preserving the important information in each application. EFD provides accurate frequency information, thus the use of EFD opens many possibilities. The method can be extended to represent other kind of shapes.

Paper Nr: 6
Title:

Empirical Mode Decomposition-based Face Recognition System

Authors:

Esteve Gallego-Jutglà, Karmele López-de-Ipiña, Pere Martí-Puig and Jordi Solé-Casals

Abstract: In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.