Miguel Ramón

A small look at the ear recognition process using a hybrid approach

The purpose of this document is to offer a combined approach in biometric analysis field, integrating some of the most known techniques using ears to recognize people. This study uses Hausdorff distance as a pre-processing stage adding sturdiness to increase the performance filtering for the subjects to use it in the testing process. Also includes the Image Ray Transform (IRT) and the Haar based classifier for the detection step. Then, the system computes Speeded Up Robust Features (SURF) and Linear Discriminant Analysis (LDA) as an input of two neural networks to recognize a person by the patterns of its ear. To show the applied theory experimental results, the above algorithms have been implemented using Microsoft C#. The investigation results showed robustness improving the ear recognition process.

Neural Networks Using Hausdorff Distance, SURF and Fisher Algorithms for Ear Recognition

The purpose of this paper is to offer an approach in the biometrics analysis field, using ears to recognize people. This study uses Hausdorff distance as a preprocessing stage adding sturdiness to increase the performance filtering for the subjects to use for testing stage of the neural network. Then, the system computes Speeded Up Robust Features (SURF) and Fisher Linear Discriminant Analysis (LDA) as an input of two neural networks to detect and recognize a person by the patterns of its ear. To show the applied theory in the experimental results; it also includes an application developed with Microsoft .net. The investigation which enhances the ear recognition process showed robustness through the integration of Hausdorff, LDA and SURF in neural networks.

Ear Recognition with Neural Networks Based on Fisher and Surf Algorithms

This paper offers an approach to biometric analysis using ears for recognition. The ear has all the assets that a biometric trait should possess. Because it is a study field in potential growth, this research offers an approach using Speeded Up Robust Features (SURF) and Fisher Linear Discriminant Analysis (LDA) as an input of two neural networks with the purpose to detect and recognize a person by the patterns of its ear. It also includes the development of an application with .net to show experimental results of the applied theory. In the preprocessing task, the system adds sturdiness using Hausdorff distance to increase the performance filtering for the subjects to use in the testing stage of the neural network. To perform this study, we worked with the help of Avilas police school (Spain), where we built a database with approximately 300 ears. The investigation results shown that the integration of LDA and SURF in neural networks can improve the ear recognition process and provide robustness in changes of illumination and perception.

A Brief Approach to the Ear Recognition Process

This paper offers an approach to biometric analysis using ears for recognition. The ear has all the assets that a biometric trait should possess. Because it is a study field in potential growth, this study offers an approach using SURF features as an input of a neural network with the purpose to detect and recognize a person by the patterns of its ear, also includes, the development of an application with .net to show experimental results of the theory applied. Ear characteristics, which are a unchanging biometric approach that does not vary with age, have been used for several years in the forensic science of recognition, thats why the research gets important value in the present. To perform this study, we worked with the help of Police School of Avila, Province of Spain, we have built a database with approximately 300 ears.

Advanced System for Management and Recognition of Minutiae in Fingerprints

This article briefly describes the advanced computer system designed for the recognition of minutiae in fingerprints digital images. The system provides both automatic and manual extraction of relevant data from the fingerprints images, storing that information in a database. Provides statistical calculations, including calculations for cumulative frequency analysis; this is an important parameter for calculating distinction rates. The system is enabled to differentiate by sex, finger, fingerprint type and sector that has been divided the dactylogram.