[09-17]kyb compliance-kyc definition in banking

Financial success for biometrics?


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Author:victim of identity theft uk    biometrics example

Keywords:kyc and compliance,    edd finance,    kyc programs,    identity verification methods,    pep list 2019,  pep government


Single Frame Face Liveness Detection from Near Infrared Image Based on LBP and GLCM
Security is ubiquitous. Need of security is the basic necessity of any individual or a system. The feeling that you are safe and everything around you is all right is imperative for a peaceful living. But in this unsafe world, when crime, terror and threats are on their peak, how can one attain that sense of security? Cross-border travel, crime and fraud are now easier than ever before. Biometric technology offers a reliable and cost effective way to manage identities for security and authentication purposes. Biometrics are automated methods of recognizing a person based on a physiological or behavioural characteristic. Among the features measured are; face, Emotion, age, gender, fingerprints, hand geometry, handwriting, iris, retina, vein, signature, DNA and voice. Biometric-based solutions are able to provide for confidential financial transactions and personal data privacy. The need for biometrics can be found in computer security, federal, state and local governments, in the military, and in commercial applications. Enterprise-wide network security infrastructures, government IDs, secure electronic banking, investing and other financial transactions, retail sales, law enforcement, and health and social services are already benefiting from these technologies. Biometric methods can also be multimodal. Biometrics do have pros and cons. They are seductive, unique hard to forge identifiers but, they are not secrets. Some biometrics are easy to steal. However, with few limitations the biometrics technology is currently on a leading edge.
This paper presents a multimodal biometrie verification system based on the following hand features: palmprint, four digitprints and four fingerprints. The features are obtained using the Karhunen-Love transform based approach, and information fusion at the matching-score level was applied. We experimented with different resolutions of the regions of interest, different numbers of features and several normalization and fusion techniques at the matching-score level. To increase the reliability of the system to spoof attacks we included an aliveness-detection module based on thermal images of the hand dor sa. The verification performance when using a system configuration with optimum parameters, i.e., resolution, number of features, normalization and fusion technique, showed an equal error rate (EER) of 0.0020%, which makes the system appropriate for the implementation of high-security biometric systems.

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