Biometric Recognition Performing in a Bioinspired System

Abstract

In this study, we propose a set of biometric recognition experiments in similar conditions to real operating systems. This implies a jump from the usual laboratory conditions to a more real situation where the amount of variability between training and testing samples is large. We present experiments with face, hand-geometry, and signature recognition training a ''universal classifier'' able to decide if two input samples belong to the same person or not. During test, we recognize samples of a different database not used during classifier training. Training with the ORL face database and testing with the AR database provides a 5.1% error rate in verification operation, while training and testing with the same database yields 2.5%. For hand-geometry databases, we obtain 4.33 and 0.16% for different and same testing and training databases, respectively. For signature recognition, we obtain 1.36 and 4.14% for different and same testing and training databases, respectively. Our proposed system implies a very low computational cost to introduce/remove a user in the database, which is a crucial point for a real operation biometric system.

A Criterion for Analysis of Different Sensor Combinations with an Application to Face Biometrics

“…An example of this situation is a surveillance camera acquiring nocturnal infrared images and the matching of these sets of images with a database of visible images of suspicious people. In some sense, this is aligned with our previous work [3], due to the mismatch between training and testing conditions. In this paper, we study the best/worst combination for sensor interoperability.…”

“…In our previous paper [3], we presented a set of experiments with a Biometric Dispersion Matcher (BDM) classifier [4] trained with a given database and tested with a different one. The goal was to obtain a good generalization capability and to avoid overfitting to a specific database.…”

Biometric Face Recognition Based on Enhanced Histogram Approach

“… Behavioral is related to a person's behavior, including writing, pacing, voice, and gait. The standard biometric face recognition system is shown in figure (1) that can be divided into the following main parts: firstly, preprocessing is implemented on the data received from sensors, then the biometric system starts by extracted the features from the received image, after that a template must be generated to be compared with the stored templates, lastly a decision will be taken to select the similar template.…”

“…However, public attitudes are becoming less negative, due in part to 9/11 and the London bombings, and to the prevalence of DNA testing. Some of these concerns have kept face recognition products from reaching their full potential, but these concerns will fall by the wayside when governments and firms acknowledge that face recognition technology is the best passive and non-intrusive recognition technology available [1,2]. The biometric identification equipment assigns a numerical value to every subject captured by hi-tech cameras.…”