Roberto Brunelli

Face recognition: features versus templates

Two new algorithms for computer recognition of human faces, one based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second based on almost-gray-level template matching, are presented. The results obtained for the testing sets show about 90% correct recognition using geometrical features and perfect recognition using template matching. >

Person identification using multiple cues

This paper presents a person identification system based on acoustic and visual features. The system is organized as a set of non-homogeneous classifiers whose outputs are integrated after a normalization step. In particular, two classifiers based on acoustic features and three based on visual ones provide data for an integration module whose performance is evaluated. A novel technique for the integration of multiple classifiers at an hybrid rank/measurement level is introduced using HyperBF networks. Two different methods for the rejection of an unknown person are introduced. The performance of the integrated system is shown to be superior to that of the acoustic and visual subsystems. The resulting identification system can be used to log personal access and, with minor modifications, as an identity verification system.<<ETX>>

Histograms analysis for image retrieval

Abstract This paper analyzes the use of histograms of low-level image features, such as color and luminance, as descriptors for image-retrieval purposes. A novel definition of histogram capacity curve taking into account the density distribution of histograms in the corresponding spaces is proposed and used to quantify the effectiveness of image descriptors and histogram dissimilarities in image retrieval applications. The results permit the design of scalable image-retrieval systems which make optimal use of computational and storage resources.

Face Recognition through Geometrical Features

Several different techniques have been proposed for computer recognition of human faces. This paper presents the first results of an ongoing project to compare several recognition strategies on a common database.

Caricatural effects in automated face perception

This paper analyzes properties of a certain class of approximation techniques — HyperBF networks — in face perception tasks. The problem of gender classification and identification is addressed using a geometrical description of faces, extracted automatically from digitized pictures of frontal views of people without facial hair. The HyperBF networks perform satisfactorily on the classification tasks and exhibit the phenomenon of caricaturing, previously reported in psychophysical experiments.

Robust estimation of correlation with applications to computer vision

Abstract In this paper we compare to the standard correlation coefficient three estimators of similarity for visual patterns which are based on the L 2 and L 1 norms. The emphasis of the comparison is on the stability of the resulting estimates. Bias, efficiency, normality and robustness are investigated through Monte Carlo simulations in a statistical task, the estimation of the correlation parameter of a binormal distribution. The four estimators are then compared on two pattern recognition tasks: people identification through face recognition and book identification from the cover image. The similarity measures based on the L 1 norm prove to be less sensitive to noise and provide better performance than those based on L 2 norm.

Efficient Image Retrieval by Examples

A currently relevant research field in information sciences is the management of non-traditional distributed multimedia databases. Two related key issues are achieving an efficient content-based query by example retrieval and a fast response time. This paper presents the architecture of a distributed image retrieval system which provides novel solutions to these key issues. In particular, a way to quantify the effectiveness of low level visual descriptors in database query tasks is presented. The results are then used to improve the system response time, an important issue when querying very large databases. A new mechanism to simplify user queries, featuring local modification of the comparison metric in the space of image descriptors, is presented and discussed.