Abstract
| Original language | English |
|---|---|
| Journal | IEEE Transactions on Information Forensics and Security |
| Volume | 15 |
| Pages (from-to) | 56-65 |
| Number of pages | 10 |
| ISSN | 1556-6013 |
| DOIs | |
| Publication status | Published - 2020 |
Keywords
- Fingerprint recognition
- Biometric capacity
- Biometric entropy
- Generative modelling
Cite this
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Fingerprint Entropy and Identification Capacity Estimation Based on Pixel-level Generative Modelling. / Yankov, Metodi Plamenov; Olsen, Martin A.; Stegmann, Mikkel Bille; Christensen, Søren Sk; Forchhammer, Søren.
In: IEEE Transactions on Information Forensics and Security, Vol. 15, 2020, p. 56-65.Research output: Contribution to journal › Journal article › Research › peer-review
TY - JOUR
T1 - Fingerprint Entropy and Identification Capacity Estimation Based on Pixel-level Generative Modelling
AU - Yankov, Metodi Plamenov
AU - Olsen, Martin A.
AU - Stegmann, Mikkel Bille
AU - Christensen, Søren Sk
AU - Forchhammer, Søren
PY - 2020
Y1 - 2020
N2 - A family of texture-based generative models for fingerprint images is proposed. The generative models are used to estimate upper bounds on the image entropy for systems with small sensor acquisition. The identification capacity of such systems is then estimated using the mutual information between different samples from the same finger. Similar to the generative model for entropy estimation, pixel-level model families are proposed for estimating similarity between fingerprint images with a given global affine transformation. These models are used for mutual information estimation, and are also adopted to compensate for local deformations between samples. Finally, it is shown that sensor sizes as small as 52x52 pixels are potentially sufficient to discriminate populations as large as the entire world population that ever lived, given that a complexity-unconstrained recognition algorithm is available which operates on the lowest possible pixel level.
AB - A family of texture-based generative models for fingerprint images is proposed. The generative models are used to estimate upper bounds on the image entropy for systems with small sensor acquisition. The identification capacity of such systems is then estimated using the mutual information between different samples from the same finger. Similar to the generative model for entropy estimation, pixel-level model families are proposed for estimating similarity between fingerprint images with a given global affine transformation. These models are used for mutual information estimation, and are also adopted to compensate for local deformations between samples. Finally, it is shown that sensor sizes as small as 52x52 pixels are potentially sufficient to discriminate populations as large as the entire world population that ever lived, given that a complexity-unconstrained recognition algorithm is available which operates on the lowest possible pixel level.
KW - Fingerprint recognition
KW - Biometric capacity
KW - Biometric entropy
KW - Generative modelling
U2 - 10.1109/TIFS.2019.2916406
DO - 10.1109/TIFS.2019.2916406
M3 - Journal article
VL - 15
SP - 56
EP - 65
JO - IEEE Transactions on Information Forensics and Security
JF - IEEE Transactions on Information Forensics and Security
SN - 1556-6013
ER -