A
Alessandra Lumini
Researcher at University of Bologna
Publications - 218
Citations - 7473
Alessandra Lumini is an academic researcher from University of Bologna. The author has contributed to research in topics: Support vector machine & Random subspace method. The author has an hindex of 42, co-authored 209 publications receiving 6685 citations. Previous affiliations of Alessandra Lumini include Missouri State University & Northwestern Polytechnical University.
Papers
More filters
Journal ArticleDOI
Local binary patterns variants as texture descriptors for medical image analysis
TL;DR: The results show that the novel variant named elongated quinary patterns (EQP) is a very performing method among those proposed in this work for extracting information from a texture in all the tested datasets.
Journal ArticleDOI
Fingerprint classification by directional image partitioning
TL;DR: This work introduces a new approach to automatic fingerprint classification in which the directional image is partitioned into "homogeneous" connected regions according to the fingerprint topology, thus giving a synthetic representation which can be exploited as a basis for the classification.
Journal ArticleDOI
Fingerprint Image Reconstruction from Standard Templates
TL;DR: The experimental results show that the reconstructed images are very realistic and that, although it is unlikely that they can fool a human expert, there is a high chance to deceive state-of-the-art commercial fingerprint recognition systems.
Journal ArticleDOI
Survey on LBP based texture descriptors for image classification
TL;DR: The aim of this work is to find the best way for describing a given texture using a local binary pattern (LBP) based approach and to compare several texture descriptors, it is shown that the proposed approach coupled with random subspace ensemble outperforms other recent state-of-the-art approaches.
Journal ArticleDOI
An improved BioHashing for human authentication
Alessandra Lumini,Loris Nanni +1 more
TL;DR: This paper introduces some ideas to improve the base BioHashing approach in order to maintain a very low equal error rate when nobody steals the Hash key, and to reach good performance also when an ''impostor'' steals theHash key.