[09-17]money laundering pep-age verification software

Biometric liveness detection based on cross modal fusion

Date:2021-09-17

kyc online

Author:enhanced due diligence examples    gdpr access

Keywords:onfido contact number,    identity proofing,    digital identity fatf,    know your customer anti money laundering,    know your customer norms,  customer identity proofing

Description:

Basic Study on Presentation Attacks against Biometric Authentication using Photoplethysmogram
Fingerprint-based recognition is widely deployed in different domains. However, the traditional fingerprint recognition systems are vulnerable to presentation attack, which utilizes an artificial replica of the fingerprint to deceive the sensors. In such scenarios, Fingerprint Liveness Detection (FLD) is required to ensure the actual presence of a live fingerprint. In this paper, a fingerprint matching method fused with liveness detection is proposed. Firstly, the similarity between two fingerprint images is calculated based on Octantal Neatest-Neighborhood Structure (ONNS), where the closest minutia to the central minutia is found from each sector of octant. Secondly, the FLD score of the fingerprint image is obtained by using the modified Residual Network (Slim-ResCNN). Finally, a score-level fusion is performed on the results of fingerprint matching and FLD by generating interaction features and polynomial features as the score feature vector. To classify whether a fingerprint image is a genuine live fingerprint or a spoof attack (including impostor live and fake fingerprints), the score feature vector is processed using logistic regression (LR) classifiers. The proposed method won the first place in the Fingerprint Liveness Detection Competition 2019 with an overall accuracy of 96.88%, which indicates it can effectively protect the fingerprint recognition systems from spoof attacks.
A novel liveness detection method using the shadow around the iris edge depending on the directions of near-infrared illumination is developed. Iris recognition system is vulnerable to deception by fake irises although iris recognition is the most reliable method in biometrics. To overcome such a problem, various liveness detection methods have been proposed: the fast Fourier spectrum method, corneal reflection method, etc. However, these liveness detection methods accept an imposter as a legitimate person by using simple methods with a printed iris image. In this study, to develop the novel liveness detection method, the brightness variation in the iris edge was investigated by changing the direction of near-infrared illuminations. From the experimental results, the suitable combinations of the directions of near-infrared illuminations were ^\circ$, ^\circ$ and ^\circ$. The brightness variation rates were calculated between ^\circ$ and ^\circ$ as $f^{45}_{avg}$, and between ^\circ$ and ^\circ$ as $f^{75}_{avg}$. The $f^{45}_{avg}$ in the live iris was between .63\%$ and .23\%$. While, the $f^{75}_{avg}$ in the live iris was over .23\%$. For the fake iris, on the other hand, the brightness variation rates for both $f^{45}_{avg}$ and $f^{75}_{avg}$ were less than \%$. Thus, the fake iris can be detected by using the difference of brightness variation rates between the live and fake irises.

Similar Articles