A
Angelo Genovese
Researcher at University of Milan
Publications - 75
Citations - 1150
Angelo Genovese is an academic researcher from University of Milan. The author has contributed to research in topics: Biometrics & Computer science. The author has an hindex of 15, co-authored 61 publications receiving 786 citations.
Papers
More filters
Journal ArticleDOI
PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition
TL;DR: PalmNet is a new method of applying Gabor filters in a CNN that uses a newly developed method to tune palmprint-specific filters through an unsupervised procedure based on Gabor responses and principal component analysis (PCA), not requiring class labels during training.
Journal ArticleDOI
A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks
TL;DR: This paper proposes the first method in the literature able to extract the coordinates of the pores from touch-based, touchless, and latent fingerprint images, and uses specifically designed and trained Convolutional Neural Networks to estimate and refine the centroid of each pore.
Journal ArticleDOI
Biometric Recognition in Automated Border Control: A Survey
Ruggero Donida Labati,Angelo Genovese,Enrique Muñoz,Vincenzo Piuri,Fabio Scotti,Gianluca Sforza +5 more
TL;DR: The biometric literature relevant to identity verification is surveyed and the best practices and biometric techniques applicable to ABC are summarized, relying on real experience collected in the field.
Journal ArticleDOI
Toward Unconstrained Fingerprint Recognition: A Fully Touchless 3-D System Based on Two Views on the Move
TL;DR: The proposed fully touchless fingerprint recognition system adopts an innovative and less-constrained acquisition setup, does not require contact with any surface or a finger placement guide, and simultaneously captures multiple images while the finger is moving, and proposes novel algorithms for computing 3-D models of the shape of a finger.
Proceedings ArticleDOI
Contactless fingerprint recognition: A neural approach for perspective and rotation effects reduction
TL;DR: A novel approach able to recover perspective deformations and improper fingertip alignments in single camera systems is presented and can effectively enhance the recognition accuracy of single-camera biometric systems.