Quality-Based Conditional Processing in Multi-Biometrics: Application to Sensor Interoperability

As biometric technology is increasingly deployed, it will be common to replace parts of operational systems with newer designs. The cost and inconvenience of reacquiring enrolled users when a new vendor solution is incorporated makes this approach difficult and many applications will require to deal with information from different sources regularly. These interoperability problems can dramatically affect the performance of biometric systems and thus, they need to be overcome. Here, we describe and evaluate the ATVS-UAM fusion approach submitted to the quality-based evaluation of the 2007 BioSecure Multimodal Evaluation Campaign, whose aim was to compare fusion algorithms when biometric signals were generated using several biometric devices in mismatched conditions. Quality measures from the raw biometric data are available to allow system adjustment to changing quality conditions due to device changes. This system adjustment is referred to as quality-based conditional processing. The proposed fusion approach is based on linear logistic regression, in which fused scores tend to be log-likelihood-ratios. This allows the easy and efficient combination of matching scores from different devices assuming low dependence among modalities. In our system, quality information is used to switch between different system modules depending on the data source (the sensor in our case) and to reject channels with low quality data during the fusion. We compare our fusion approach to a set of rule-based fusion schemes over normalized scores. Results show that the proposed approach outperforms all the rule-based fusion schemes. We also show that with the quality-based channel rejection scheme, an overall improvement of 25% in the equal error rate is obtained.Comment: Published at IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Human

Comparación de tres metodologías para la construcción de intervalos de confianza de los índices de capacidad del proceso bajo datos autocorrelacionados

Los ındices de  capacidad de  un  proceso han  sido  ampliamente utilizados en  la industria, los cuales suministran una  informacion numerica acerca de como el proceso se a justa a unos  l´ımites de especificaci´on establecidos.   Los  procedimientos existentes para construir intervalos de  confianza para los ındices  de  capacidad Cpm y  C en  procesos estacionarios  gaussianos muestran ba jos porcenta jes de cobertura. Este artıculo presenta dos  metodologıas alternativas para construir inter- valos  de  confianza para los ´ındices  C pm y C pmk, ademas  de  los ındices  Cp y  C,  en  procesos estacionarios  gaussianos.   La  comparacion de  las  tres metodolog´ıas  se  realiza mediante  simulacion, analizando el  pk porcenta je  de cobertura para procesos autorregresivos de orden uno

Voxceleb-ESP: preliminary experiments detecting Spanish celebrities from their voices

This paper presents VoxCeleb-ESP, a collection of pointers and timestamps to YouTube videos facilitating the creation of a novel speaker recognition dataset. VoxCeleb-ESP captures real-world scenarios, incorporating diverse speaking styles, noises, and channel distortions. It includes 160 Spanish celebrities spanning various categories, ensuring a representative distribution across age groups and geographic regions in Spain. We provide two speaker trial lists for speaker identification tasks, each of them with same-video or different-video target trials respectively, accompanied by a cross-lingual evaluation of ResNet pretrained models. Preliminary speaker identification results suggest that the complexity of the detection task in VoxCeleb-ESP is equivalent to that of the original and much larger VoxCeleb in English. VoxCeleb-ESP contributes to the expansion of speaker recognition benchmarks with a comprehensive and diverse dataset for the Spanish language

On the Use of Deep Feedforward Neural Networks for Automatic Language Identification

In this work, we present a comprehensive study on the use of deep neural networks (DNNs) for automatic language identification (LID). Motivated by the recent success of using DNNs in acoustic modeling for speech recognition, we adapt DNNs to the problem of identifying the language in a given utterance from its short-term acoustic features. We propose two different DNN- based approaches. In the first one, the DNN acts as an end-to-end LID classifier, receiving as input the speech features and providing as output the estimated probabilities of the target languages. In the second approach, the DNN is used to extract bottleneck features that are then used as inputs for a state-of-the-art i-vector system. Experiments are conducted in two different scenarios: the complete NIST Language Recognition Evaluation dataset 2009 (LRE’09) and a subset of the Voice of America (VOA) data from LRE’09, in which all languages have the same amount of training data. Results for both datasets demonstrate that the DNN-based systems significantly outperform a state-of-art i-vector system when dealing with short-duration utterances. Furthermore, the combination of the DNN-based and the classical i-vector system leads to additional performance improvements (up to 45% of relative improvement in both EER and Cavg on 3s and 10s conditions, respectively)

Determinación de la presencia de Helicobacter pylori mediante un dispositivo cromatográfico específico, en cerdos sacrificados en el Frigorífico Vijagual.

Pathologies of uncertain etiology manifested by gastric ulcers have been observed in pig. This research paper shows the association of Helicobacter sp. as the main causing agent of this disorder by studying a sample of 100 pigs slaughtered at the Vijagual abattoir in the Bucaramanga city. Sera samples were analyzed and samples of gastric tissue were processed by histopathological methods using Hematoxiline-Eosine and Warthin-Starry to confirm the correlation between spiral bacteria and gastric lesions. Blood sera of positive pigs in the histopathological examination were examined by a specific chromatographic device or immunoassay (ACON ®). Analysis of results was based on a experimental model describing the number of positive and negative pigs to Helicobacter sp. bacteria. Histopathologically, 53% of the animals were positive and 47% were negative. The species of bacteria assosiated were Helicobacter pylori (60,3%) and Helicobacter hellmanii (39,6%). For the serological procedure, 25 positive and 25 negative samples to Helicobacter sp. were analyzed. A reliability of 92% with the specific test and 75% of negative confirmation was found. The statistical test of Chi Square was utilized to evaluate the association of Helicobacter sp. to gastric lesions. Standardization was carried out by the ACON ®specific chromatographic device. This test is designed for the detection of IgG antibodies against Helicobacter pylori in human serum for the diagnosis of gastrointestinal diseases. Based on this method, reliability and specificity of the test was demonstrated for the determination of Helicobacter pylori in sera of pigs with gastric lesions. This technique represents a precise aid for the management of gastric clinical malfunctions that are frequentin porcine exploitations nowadays.    En las unidades de producción porcina se han presentado patologías de etiología incierta que indican la presencia de lesiones gástricas compatibles con infección por Helicobacter sp. Ante la necesidad de determinar la asociación entre los hallazgos clínicos y la infección con Helicobacter sp., el trabajo se propuso confirmar por serología la presencia de la bacteria. El desarrollo de la investigación utilizó una muestra de 100 cerdos de sacrificio del Frigorífico Vijagual, de la ciudad de Bucaramanga, realizando el muestreo sanguíneo y de tejido gástrico que fue procesado histopatológicamente con hematoxilina-eosina y warthin-starry para correlacionar la presencia de bacterias espiraladas con lesiones gástricas, y serológicamente confirmando la presencia de Helicobacter pylori mediante un dispositivo cromatográfico específico o inmunoensayo (ACONâ) en cerdos positivos al examen histopatológico.El análisis de resultados se basó en un modelo experimental que describe el número de cerdos positivos y negativos a bacterias del género helicobacter sp., donde histopatológicamente se hallaron 53% positivos y 47% negativos; se identificaron 60,3% compatibles a helicobacter pylori y 39,6% a helicobacter hellmanii. Para el procedimiento serológico se tomaron 25 sueros positivos y 25 negativos a Helicobacter sp., lo que expresó un 92% de confiabilidad con la prueba específica y un 76% que confirma la seronegatividad. Estos resultados utilizaron la prueba estadística de chi cuadrada para evaluar la asociación de la presencia de Helicobacter sp. y lesiones gástricas mediante la estandarización del dispositivo cromatográfico específico ACONâ.Esta prueba está diseñada para la detección de anticuerpos IgG contra Helicobacter pylori en suero humano para el diagnóstico de enfermedades gastrointestinales. Apoyados en esto hemos demostrado la confiabilidad y especificidad de la prueba para la determinar la presencia de Helicobacter pylori en suero de cerdos con lesiones gástricas, proporcionando la ayuda precisa para el manejo de cuadros clínicos gástricos, muy comunes en las actuales explotaciones porcinas

Phoneme and Sub-Phoneme T-Normalization for Text-Dependent Speaker Recognition

Test normalization (T-Norm) is a score normalization technique that is regularly and successfully applied in the context of text-independent speaker recognition. It is less frequently applied, however, to text-dependent or textprompted speaker recognition, mainly because its improvement in this context is more modest. In this paper we present a novel way to improve the performance of T-Norm for text-dependent systems. It consists in applying score TNormalization at the phoneme or sub-phoneme level instead of at the sentence level. Experiments on the YOHO corpus show that, while using standard sentence-level T-Norm does not improve equal error rate (EER), phoneme and sub-phoneme level T-Norm produce a relative EER reduction of 18.9% and 20.1% respectively on a state-of-the-art HMM based textdependent speaker recognition system. Results are even better for working points with low false acceptance rates

Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification

Signal-quality awareness has been found to increase recognition rates and to support decisions in multisensor environments significantly. Nevertheless, automatic quality assessment is still an open issue. Here, we study the orientation tensor of fingerprint images to quantify signal impairments, such as noise, lack of structure, blur, with the help of symmetry descriptors. A strongly reduced reference is especially favorable in biometrics, but less information is not sufficient for the approach. This is also supported by numerous experiments involving a simpler quality estimator, a trained method (NFIQ), as well as the human perception of fingerprint quality on several public databases. Furthermore, quality measurements are extensively reused to adapt fusion parameters in a monomodal multialgorithm fingerprint recognition environment. In this study, several trained and nontrained score-level fusion schemes are investigated. A Bayes-based strategy for incorporating experts past performances and current quality conditions, a novel cascaded scheme for computational efficiency, besides simple fusion rules, is presented. The quantitative results favor quality awareness under all aspects, boosting recognition rates and fusing differently skilled experts efficiently as well as effectively (by training).Comment: Published at IEEE Transactions on Information Forensics and Securit