Face Recognition Based on Texture Descriptors

In this chapter, the performance of different texture descriptor algorithms used in face feature extraction tasks are analyzed. These commonly used algorithms to extract texture characteristics from images, with quite good results in this task, are also expected to provide fairly good results when used to characterize the face in an image. To perform the testing task, an AR face database, which is a standard database that contains images of 120 people, was used, including 70 images with different facial expressions and 30 with sunglasses, and all of them with different illumination intensity. To train the recognition system from one to seven images were used for each person. Different classifiers like Euclidean distance, cosine distance, and support vector machine (SVM) were also used, and the results obtained were higher than 98% for classification, achieving a good performance in verification task. This chapter was also compared with other schemes, showing the effectiveness of all of them

A Compact Digital Gamma-tone Filter Processor

Area consumption is one of the most important design constrains in the development of compact digital systems. Several authors have proposed making compact Cochlear Implant processors using Gamma-tone filter banks. These model aspects of the cochlea spectral filtering. A good area-efficient design of the Gamma-tone Filter Bank could reduce the amount of circuitry allowing patients to wear these cochlear implants more easily. In consequence, many authors have reduced the area by using the minimum number of registers when implementing this type of filter. However, critical paths limit their performance. Here a compact Gamma-tone Filter processor, formulated using the impulse invariant transformation together with a normalization method, is presented. The normalization method in the model guarantees the same precision for any filter order. In addition, area resources are kept low due to the implementation of a single Second Order Section (SOS) IIR stage for processing several SOS IIR stages and several channels at different times. Results show that the combination of the properties of the model and the implementation techniques generate a processor with high processing speed, expending less resources than reported in the literature.Collaboration with Sanchez-Rivera, related to, but not funded by, EPSRC grant EP/G062609/

System for creating and authentication credentials

System for creating and authentication credentialsThis article present a system for creating and credential authentication personal identi- fication (ID) safe, using one-dimensional and two-dimensional barcodes, data encryption and symmetric key technique watermark. To do this, a watermark is inserted on the photo of the user, generated from a unique identification code which will be available in printed credential so that at the time of validation of the credential is carried by calculating the cross-correlation between the watermark contained in the photograph of the user and the watermark calculated at the time of validation using the same unique identification code. We show that proper selection of parameters for inserting the watermark: length, gain, position of the watermark and decision threshold, are essential to ensure the proper functioning of the proposed scheme, ensuring maintain sufficient quality in visual image to the user recognition and in turn be robust enough to withstand the attack of converting digital-analog (D / A) and analog-digital (A/D)

Basic definitions for discrete modeling of computer worms epidemics

The information technologies have evolved in such a way that communication between computers or hosts has become common, so much that the worldwide organization (governments and corporations) depends on it; what could happen if these computers stop working for a long time is catastrophic. Unfortunately, networks are attacked by malware such as viruses and worms that could collapse the system. This has served as motivation for the formal study of computer worms and epidemics to develop strategies for prevention and protection; this is why in this paper, before analyzing epidemiological models, a set of formal definitions based on set theory and functions is proposed for describing 21 concepts used in the study of worms. These definitions provide a basis for future qualitative research on the behavior of computer worms, and quantitative for the study of their epidemiological models.Las tecnologías de la información han evolucionado de tal manera que la comunicación entre computadoras o hosts se ha vuelto algo común, hasta el punto que la organización a nivel mundial (gobiernos y grandes empresas) depende de esto; lo que pasaría si estas computadoras dejaran de funcionar por un periodo de tiempo largo es catastrófico. Desgraciadamente, las redes de hosts son blanco de ataques por malware como virus y gusanos informáticos que podrían colapsar el sistema. Esto ha servido como motivación para el estudio formal de los gusanos informáticos y sus epidemias con el fin de idear estrategias de prevención y protección; por ello en el presente artículo, y previo al análisis de modelos epidemiológicos, se proponen un conjunto de definiciones formales basadas en la teoría de conjuntos y funciones que permiten describir 21 conceptos utilizados en el estudio de los gusanos informáticos. Estas definiciones servirán de base para futuras investigaciones cualitativas sobre el comportamiento de los gusanos informáticos, y cuantitativas para el estudio de sus modelos epidemiológicos

Early Fire Detection on Video Using LBP and Spread Ascending of Smoke

This paper proposes a methodology for early fire detection based on visual smoke characteristics such as movement, color, gray tones and dynamic texture, i.e., diverse but representative and discriminant characteristics, as well as its ascending expansion, which is sequentially processed to find the candidate smoke regions. Thus, once a region with movement is detected, the pixels inside it that are smoke color are estimated to obtain a more detailed description of the smoke candidate region. Next, to increase the system efficiency and reduce false alarms, each region is characterized using the local binary pattern, which analyzes its texture and classifies it by means of a multi-layer perceptron. Finally, the ascending expansion of the candidate region is analyzed and those smoke regions that maintain or increase their ascending growth over a time span are considered as a smoke regions, and an alarm is triggered. Evaluations were performed using two different classifiers, namely multi-Layer perceptron and the support vector machine, with a standard database smoke video. Evaluation results show that the proposed system provides fire detection accuracy of between 97.85% and 99.83%