Systematic methods for the computation of the directional fields and singular points of fingerprints

The first subject of the paper is the estimation of a high resolution directional field of fingerprints. Traditional methods are discussed and a method, based on principal component analysis, is proposed. The method not only computes the direction in any pixel location, but its coherence as well. It is proven that this method provides exactly the same results as the "averaged square-gradient method" that is known from literature. Undoubtedly, the existence of a completely different equivalent solution increases the insight into the problem's nature. The second subject of the paper is singular point detection. A very efficient algorithm is proposed that extracts singular points from the high-resolution directional field. The algorithm is based on the Poincare index and provides a consistent binary decision that is not based on postprocessing steps like applying a threshold on a continuous resemblance measure for singular points. Furthermore, a method is presented to estimate the orientation of the extracted singular points. The accuracy of the methods is illustrated by experiments on a live-scanned fingerprint databas

Likelihood-Ratio-Based Biometric Verification

The paper presents results on optimal similarity measures for biometric verification based on fixed-length feature vectors. First, we show that the verification of a single user is equivalent to the detection problem, which implies that, for single-user verification, the likelihood ratio is optimal. Second, we show that, under some general conditions, decisions based on posterior probabilities and likelihood ratios are equivalent and result in the same receiver operating curve. However, in a multi-user situation, these two methods lead to different average error rates. As a third result, we prove theoretically that, for multi-user verification, the use of the likelihood ratio is optimal in terms of average error rates. The superiority of this method is illustrated by experiments in fingerprint verification. It is shown that error rates below 10/sup -3/ can be achieved when using multiple fingerprints for template construction

A Multi-Scale Approach to Directional Field Estimation

This paper proposes a robust method for directional field estimation from fingerprint images that combines estimates at multiple scales. The method is able to provide accurate estimates in scratchy regions, while at the same time maintaining correct estimates around singular points. Compared to other methods, the penalty for detecting false singular points is much smaller, because this does not deteriorate the directional field estimate

Likelihood Ratio-Based Detection of Facial Features

One of the first steps in face recognition, after image acquisition, is registration. A simple but effective technique of registration is to align facial features, such as eyes, nose and mouth, as well as possible to a standard face. This requires an accurate automatic estimate of the locations of those features. This contribution proposes a method for estimating the locations of facial features based on likelihood ratio-based detection. A post-processing step that evaluates the topology of the facial features is added to reduce the number of false detections. Although the individual detectors only have a reasonable performance (equal error rates range from 3.3% for the eyes to 1.0% for the nose), the positions of the facial features are estimated correctly in 95% of the face images

A Reinforcement Learning Agent for Minutiae Extraction from Fingerprints

In this paper we show that reinforcement learning can be used for minutiae detection in fingerprint matching. Minutiae are characteristic features of fingerprints that determine their uniqueness. Classical approaches use a series of image processing steps for this task, but lack robustness because they are highly sensitive to noise and image quality. We propose a more robust approach, in which an autonomous agent walks around in the fingerprint and learns how to follow ridges in the fingerprint and how to recognize minutiae. The agent is situated in the environment, the fingerprint, and uses reinforcement learning to obtain an optimal policy. Multi-layer perceptrons are used for overcoming the difficulties of the large state space. By choosing the right reward structure and learning environment, the agent is able to learn the task. One of the main difficulties is that the goal states are not easily specified, for they are part of the learning task as well. That is, the recognition of minutiae has to be learned in addition to learning how to walk over the ridges in the fingerprint. Results of successful first experiments are presented

Spectral representation of fingerprints

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and directions suffering from various deformations such as translation, rotation and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with a template protection scheme, which requires a fixed-length feature vector. This paper introduces the idea and algorithm of spectral minutiae representation. A correlation based spectral minutiae\ud matching algorithm is presented and evaluated. The scheme shows a promising result, with an equal error rate of 0.2% on manually extracted minutiae

Perfil lipídico, poder antirradicalario y propiedades antimicrobianas del aceite de Syzygium aromaticum

In this investigation cold pressed clove (Syzygium aromaticum) oil (CO) was studied for its lipid classes, fatty acid profiles and tocol contents. The radical scavenging potential and antimicrobial properties of CO were also evaluated. The levels of neutral lipids in CO was the highest (ca. 94.7% of total lipids), followed by glycolipids and phospholipids. The main fatty acids in CO were linoleic and oleic, which comprise together ca. 80% of total fatty acids. Stearic and palmitic acids were the main saturated fatty acids. a- and γ-tocopherols and d-tocotrienol were the main tocols. CO quenched 70% of DPPH• radicals after 1 h, while extra virgin olive oil was able to quench only 45%. ESR measurements also showed the same pattern, wherein CO quenched 57% of galvinoxyl radical and olive oil deactivated about 38%. The results of antimicrobial properties revealed that CO inhibited the growth of all tested microorganisms. CO had a drastic effect on the biosynthesis of protein and lipids in the cells of B. subtilis. In consideration of is tpotential utilization, detailed knowledge on the composition and functional properties of CO is of major importance.Se ha estudiado el aceite de clavo (Syzygium aromaticum) obtenido mediante presión en frío (CO) y sus diferentes clases de lípidos, perfiles de ácidos grasos y contenido en tocoles. También se evaluó el potencial captador de radicales y las propiedades antimicrobianas del CO. Los niveles de lípidos neutros en CO fue mayoritario (aprox. 94,7% de lípidos totales), seguido de glicolípidos y fosfolípidos. Los principales ácidos grasos en CO fueron linoleico y oleico, que comprenden juntos el 80% de ácidos grasos totales. Los ácidos esteárico y palmítico fueron los principales ácidos grasos saturados. α- y γ-Tocoferol y δ-Tocotrienol fueron los principales tocoles. CO atrapó el 70% de los radicales DPPH• después de 1 h, mientras que el aceite de oliva virgen extra fue capaz de atrapar sólo el 45%. Las medidas de ESR también mostraron el mismo patrón, en el que CO inactivó 57% de radicales galvinoxil os y el aceite de olive desactivó aproximadamente el 38%. Los resultados de las propiedades antimicrobianas revelaron que el CO inhibió el crecimiento de todos los microorganismos ensayados. CO mostró un efecto drástico en la biosíntesis de proteínas y lípidos en las células de B. subtilis. En relación al potencial utilización, el conocimiento detallado de la composición y propiedades funcionales de CO es de gran importancia

Fingerprint Verification Using Spectral Minutiae Representations

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points

A Correlation-Based Fingerprint Verification System

In this paper, a correlation-based fingerprint verification system is presented. Unlike the traditional minutiae-based systems, this system directly uses the richer gray-scale information of the fingerprints. The correlation-based fingerprint verification system first selects appropriate templates in the primary fingerprint, uses template matching to locate them in the secondary print, and compares the template positions of both fingerprints. Unlike minutiae-based systems, the correlation-based fingerprint verification system is capable of dealing with bad-quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions. Experiments have shown that the performance of this system at the moment is comparable to the performance of many other fingerprint verification systems