Textural features for fingerprint liveness detection Article Swipe
The main topic ofmy research during these three years concerned biometrics and in particular \nthe Fingerprint Liveness Detection (FLD), namely the recognition of fake fingerprints. \nFingerprints spoofing is a topical issue as evidenced by the release of the latest iPhone and \nSamsung Galaxy models with an embedded fingerprint reader as an alternative to passwords. \nSeveral videos posted on YouTube show how to violate these devices by using fake \nfingerprints which demonstrated how the problemof vulnerability to spoofing constitutes a \nthreat to the existing fingerprint recognition systems. \nDespite the fact that many algorithms have been proposed so far, none of them showed \nthe ability to clearly discriminate between real and fake fingertips. In my work, after a study \nof the state-of-the-art I paid a special attention on the so called textural algorithms. I first \nused the LBP (Local Binary Pattern) algorithm and then I worked on the introduction of the \nLPQ (Local Phase Quantization) and the BSIF (Binarized Statistical Image Features) algorithms \nin the FLD field. \nIn the last two years I worked especially on what we called the “user specific” problem. \nIn the extracted features we noticed the presence of characteristic related not only to the \nliveness but also to the different users. We have been able to improve the obtained results \nidentifying and removing, at least partially, this user specific characteristic. \nSince 2009 the Department of Electrical and Electronic Engineering of the University of \nCagliari and theDepartment of Electrical and Computer Engineering of the ClarksonUniversity \nhave organized the Fingerprint Liveness Detection Competition (LivDet). I have been \ninvolved in the organization of both second and third editions of the Fingerprint Liveness \nDetection Competition (LivDet 2011 and LivDet 2013) and I am currently involved in the acquisition \nof live and fake fingerprint that will be inserted in three of the LivDet 2015 datasets.
Related Topics
Concepts
Metadata
- Type
- dissertation
- Language
- en
- OA Status
- green
- References
- 50
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W897199356
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W897199356Canonical identifier for this work in OpenAlex
- Title
-
Textural features for fingerprint liveness detectionWork title
- Type
-
dissertationOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-04-27Full publication date if available
- Authors
-
Luca GhianiList of authors in order
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://hdl.handle.net/11584/266594Direct OA link when available
- Concepts
-
Liveness, Spoofing attack, Fingerprint (computing), Biometrics, Computer science, Password, Artificial intelligence, Fingerprint recognition, Pattern recognition (psychology), Computer vision, Computer security, Theoretical computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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50Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Electronic | 212 |
| abstract_inverted_index.University | 216 |
| abstract_inverted_index.algorithms | 84 |
| abstract_inverted_index.biometrics | 10 |
| abstract_inverted_index.especially | 159 |
| abstract_inverted_index.partially, | 201 |
| abstract_inverted_index.Competition | 233, 251 |
| abstract_inverted_index.Engineering | 213, 224 |
| abstract_inverted_index.Fingerprint | 14, 230, 249 |
| abstract_inverted_index.Statistical | 146 |
| abstract_inverted_index.algorithms. | 121 |
| abstract_inverted_index.alternative | 48 |
| abstract_inverted_index.constitutes | 72 |
| abstract_inverted_index.fingerprint | 44, 77, 268 |
| abstract_inverted_index.fingertips. | 102 |
| abstract_inverted_index.recognition | 20, 78 |
| abstract_inverted_index.specific” | 166 |
| abstract_inverted_index.demonstrated | 65 |
| abstract_inverted_index.discriminate | 97 |
| abstract_inverted_index.introduction | 136 |
| abstract_inverted_index.organization | 240 |
| abstract_inverted_index.Quantization) | 141 |
| abstract_inverted_index.the \nLPQ | 138 |
| abstract_inverted_index.theDepartment | 219 |
| abstract_inverted_index.vulnerability | 69 |
| abstract_inverted_index.a \nthreat | 73 |
| abstract_inverted_index.characteristic | 176 |
| abstract_inverted_index.study \nof | 108 |
| abstract_inverted_index.field. \nIn | 152 |
| abstract_inverted_index.first \nused | 123 |
| abstract_inverted_index.showed \nthe | 93 |
| abstract_inverted_index.state-of-the-art | 110 |
| abstract_inverted_index.and \nSamsung | 38 |
| abstract_inverted_index.of \nCagliari | 217 |
| abstract_inverted_index.problem. \nIn | 167 |
| abstract_inverted_index.the \nliveness | 181 |
| abstract_inverted_index.algorithms \nin | 149 |
| abstract_inverted_index.been \ninvolved | 237 |
| abstract_inverted_index.acquisition \nof | 264 |
| abstract_inverted_index.particular \nthe | 13 |
| abstract_inverted_index.systems. \nDespite | 79 |
| abstract_inverted_index.fake \nfingerprints | 63 |
| abstract_inverted_index.Liveness \nDetection | 250 |
| abstract_inverted_index.passwords. \nSeveral | 50 |
| abstract_inverted_index.results \nidentifying | 196 |
| abstract_inverted_index.characteristic. \nSince | 205 |
| abstract_inverted_index.ClarksonUniversity \nhave | 227 |
| abstract_inverted_index.fingerprints. \nFingerprints | 23 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5024106533 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile |