Filippo Sorbello

Fingerprint Image Enhancement Using Directional Morphological Filter

Fingerprint images quality enhancement is a topic phase to ensure good performance in an automatic fingerprint identification system (AFIS) based on minutiae matching. In this paper a new fingerprint enhancement algorithm based on morphological filter is introduced. The algorithm is based on three steps: directional decomposition, morphological filter and composition. The performance of the proposed approach has been evaluated on two sets of images: the first one is DB3 database from Fingerprint Verification Competition (FVC) and the second one is self collected using an optical scanner

A Concurrent Neural Classifier for HTML Documents Retrieval

A neural based multi-agent system for automatic HTML pages retrieval is presented. The system is based on the EαNet architecture, a neural network having good generalization capabilities and able to learn the activation function of its hidden units. The starting hypothesis is that the HTML pages are stored in networked repositories. The system goal is to retrieve documents satisfying a user query and belonging to a given class (i.e. documents containing the word “football” and talking about “Sports”). The system is composed by three interacting agents: the EαNet Neural Classifier Mobile Agent, the Query Agent, and the Locator Agent. The whole system was successfully implemented exploiting the Jade platform features and facilities. The preliminary experimental results show a good classification rate: in the best case a classification error of 9.98% is reached.

A Novel Iris Recognition System based on Micro-Features

In this paper a novel approach for iris recognition system based on iris micro-features is proposed. The proposed system follows the minutiae based approach developed for fingerprint recognition systems. The proposed system uses four iris micro-features, considered as minutiae, for identification. The individualized characteristics are nucleus, collarette, valleys and radius. Iris recognition is divided in three main phases: image preprocessing, micro-features extraction and matching. The algorithm has been tested on CASIA v1.0 iris image database obtaining an high accuracy. The obtained experimental results have been analyzed and compared with the Daugman based approach.

Fingerprint Registration Using Specialized Genetic Algorithms

One of the most common problem to realize a robust matching algorithm in an automated fingerprint identification system (AFIS) is the images registration. In this paper a fingerprints registration method based on a specialized genetic algorithm (GA) is proposed. A global transformation between two fingerprint images is performed using genetic data evolutions based on specialized mutation rate and solution refining. An AFIS including the above method has been developed and tested on two different fingerprint databases: NIST 4 ink-on-paper and self optical scanned. The obtained experimental results show that the proposed approach is comparable with literature systems working on medium quality fingerprints

An integrated neural concurrent system for pattern recognition: basic element

The article outlines a methodology to automatically select a neural-based pattern classifier. A set of neural-based specialized pattern recognizers is generated, trained and successively it is automatically chosen, among them, one which has the best generalization capabilities according to a quality index, that does not require the use of any test set. Furthermore, it is illustrated the architecture of a basic element, based on the EaNet neural classifier, of a more complex framework that will be designed for concurrent pattern recognition in networked repositories of patterns. The effectiveness of the proposed approach has been tested as an example on the NIST Special database 19 of handwritten characters images and it has also been verified using the traditional technique of the test set. For completeness, the methodology has been also tested using a traditional neural feed-forward classifier using sigmoids as activation function of its units belonging to the hidden layer. Experimental results show good performance of the proposed methodology.