Javier Preciozzi

SMOS images restoration from L1A data: A sparsity-based variational approach

Data degradation by radio frequency interferences (RFI) is one of the major challenges that SMOS and other interferometers radiometers missions have to face. Although a great number of the illegal emitters were turned off since the mission was launched, not all of the sources were completely removed. Moreover, the data obtained previously is already corrupted by these RFI. Thus, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this work we propose a variational approach to recover a super-resolved, denoised brightness temperature map based on two spatial components: an image u that models the brightness temperature and an image o modeling the RFI. The approach is totally new to our knowledge, in the sense that it is directly and exclusively based on the visibilities (L1a data), and thus can also be considered as an alternative to other brightness temperature recovery methods.

An a-contrario Approach for Face Matching

In this work we focused in the matching stage of a face recognition system. These systems are used to identify an unknown person or to validate a claimed identity. In the face recognition field it is very common to innovate in the extracted features of a face and use a simple threshold on the distance between samples in order to perform the validation of a claimed identity. In this work we present a novel strategy based in the a-contrario framework in order to improve the matching stage. This approach results in a validation threshold that is automatically adapted to the data and allows to predict the performance of the system in advance. We perform several experiments in order to validate this novel strategy using different databases and show its advantages over using a simple threshold over the distances.

Sparsity-based restoration of SMOS images in the presence of outliers

Estimates of soil moisture and surface salinity are of significant importance to improve meteorological and climate prediction. The SMOS mission monitor these quantities, by measuring the brightness temperature by means of L-band aperture synthesis interferometry. Despite the L-band being reserved for Earth and space exploration, SMOS images reveal large number of strong outliers, produced by illegal antennas emitting in this band. In this work we propose a variational approach to recover a super-resolved, denoised brightness temperature map. The measurements are modeled as the superposition of three super-resolved components in the spatial domain: the target brightness temperature map u, an image o modeling the outliers, and Gaussian noise n. This decomposition allows to isolate each of its constituent parts, thanks to a sparsity operator that acts on o, and a bounded variation prior on u that extrapolates its spectrum promoting a non-oscillating behavior. The proposed model is interesting in itself, as it is general enough to be applied to other restoration problems. Experiments on real and synthetic data confirm the suitability of the proposed approach.

Face matching with an a contrario false detection control

All biometric systems have two major functions: the identification of a given template on a biometric database and the verification that two templates correspond to the same subject. Although in both operations the response confidence of the system is of great importance, in the verification process it becomes crucial. Indeed we want to determine, with a very low error, whether two templates correspond to the same subject or not. Most of the work devoted to biometrics are related to other stages of the process: the preprocessing, feature extraction or even the definition of robust metrics to compare them. Nevertheless, few works exist on the criteria used to the acceptance of a matching between two templates. In this work we focus on this decision criterion: we introduce a novel strategy based on the a contrario framework. We show several advantages of using this framework in the context of biometrics: it is automatically adapted to the data, it allows us to control the performance of the system in advance and can be used directly in the matching stage not requiring a prior training stage. In order to show the framework on a practical situation, we implement a face recognition system. We perform several experiments to validate this novel strategy using different databases, both private and public. Also the robustness of this technique is evaluated using different features and metrics. The results show that the proposed approach outperforms classic methods, with a consistent theory behind it, that can be naturally adapted to any biometric system.

Recognizing Infants and Toddlers over an On-Production Fingerprint Database

It is widely known that biometric systems based on adults fingerprints have reached an outstanding performance when compared against other biometric traits. This explains their extensive use by governmental agencies in charge of citizen identification. Nevertheless, the performance is highly degraded when fingerprints of newborns or toddlers are used. In this work, we analyze the performance of existing solutions (both at sensor and matching level) using 45000 infants fingerprints taken from an on-production civilian database. We also propose a solution by zooming the input fingerprints with an interpolation factor based on ridges distances. The developed solution shows improvements in both fingerprint quality (NFIQ 2.0) as well as recognition performance.

A Statistical Approach to Reliability Estimation for Fingerprint Recognition

In this work we focus in the reliability estimation of biometric systems output. We explain why this is a very important problem when deploying a biometric system and face it using a statistical approach. In particular, we present a solution based in the a- contrario approach widely used in the image processing field. We show how this strategy could be adapted and its key advantages with respect to other state-of-the-art reliability measures. A comprehensive set of experiments is used to validate the approach, using different fingerprints databases, matching systems, and comparing the performance with other state-of-the-art confidence measure strategies.

Evaluation of a face recognition system performance's variation on a citizen passports database

Face recognition systems (FRS) have been widely studied and the performances reported are very high in the standard databases used for comparison. In this work we present a FRS that achieves state of the art results in these databases and show its performances variation when tested in a field trial using a citizen identification database. To accomplish this, a set of experiments are proposed. These include increasing the size of the database, using subsets that include a time difference of one to ten years between the query samples and those enrolled in the system and finally using different subsets of the same database. Discussion on these experiments and conclusions are presented.

First neuronavigation experiences in Uruguay

Neuronavigation is the application of image guidance to neurosurgery where the position of a surgical tool can be displayed on a preoperative image. Although this technique has been used worldwide in the last ten years, it was never applied in Uruguay due to its cost. In an ongoing project, the Engineering Faculty (Universidad de la República), the Hospital de Clínicas (Medicine Faculty - Universidad de la República) and the Regional Hospital of Tacuarembó are doing the first experimental trials in neuronavigation. In this project, a prototype based on optical tracking equipment and the open source software IGSTK (Image Guided Surgery Toolkit) is under development and testing.