Santiago Cruz-Llanas

Minutiae extraction scheme for fingerprint recognition systems

A complete minutiae extraction scheme for automatic fingerprint recognition systems is presented. The proposed method uses improving alternatives for the image enhancement process, leading consequently to an increase in the reliability in the minutiae extraction task. In the first stages, image normalization and the orientation field of the fingerprint are calculated. The local orientation of the ridges serve as parameter for the next processing stages. Details of the adaptive morphological filtering used for ridge extraction and background noise elimination are described. Evaluation results are obtained from both inked and scanned fingerprints. Conclusions in terms of Goodness Index (GI), which compares the results obtained by automatic minutiae extraction with manually extracted ones, are provided in order to test the global performance of this approach.

From Biometrics Technology to Applications Regarding Face, Voice, Signature and Fingerprint Recognition Systems

In this chapter, several biometric recognition systems, based on voice, fingerprint, face and signature are presented. The description of the state-of-the-art technologies regarding these biometric characteristics is widely faced. Minutiae extraction-based fingerprint matching, GMM-based speaker verification, on-line HMM-based signature verification, and PCA- or LDA-based face recognition are quoted. We will also focus on multimodality and data fusion in biometric systems; finally, some application strategies and some real-world demos are described.

An Improved Image Enhancement Scheme for Fingerprint Minutiae Extraction in Biometric Identification

In this paper a complete algorithmic scheme for automatic fingerprint recognition is presented. In [3] an identification/verification system is described. The whole recognition process is accomplished in two stages: in the first one, biometric characteristics of fingerprints are extracted (characteristics denoted as minutiae, which represent basically the beginning, end or bifurcation of a ridge), and in the second stage, those fingerprints will be matched with templates belonging to the test database. In this paper, some improving alternatives regarding the first stage, namely the image enhancement process, are proposed, consequently leading to an increase of the reliability in the minutiae extraction stage. Conclusions in terms of Goodness Index (GI) are provided in order to test the global performance of this system.

Comparison of feature extraction techniques in automatic face recognition systems for security applications

The paper is aimed at analyzing the performance of two different state-of-the-art automatic face recognition systems. One of the key issues regarding face recognition is the election of convenient features for representing identity in facial images. Multivariate analysis and Gabor analysis are alternative methods for accomplishing this feature extraction stage. Consequently, two different approaches to the face recognition problem, one based on multivariate analysis, the other on Gabor analysis, are proposed. A brief review of the theoretical foundations of both systems, together with some tests conducted for comparison, are addressed.

A comparative evaluation of global representation-based schemes for face verification

This paper is focused on algorithmic issues for biometric face verification (i.e., given an image of the face and an identity claim, decide whether they correspond to each other or not). Several alternatives for geometric normalization of images, photometric normalization, dimensionality reduction and similarity measures are proposed and compared using the XM2VTS database and the associated Lausanne protocol [K. Messer et al., 1999], [J. Luettin et al., 1998]. Experiments under this particular framework show that best verification results are obtained when holistic approaches for face recognition (such as eigenfaces or fisherfaces) are combined with techniques traditionally associated to local feature-based approaches, such as Gabor decompositions.