Protection of Patient Identity and Privacy using Vascular Biometrics

Biometric systems are being used in hospitals to streamline patient registration and identification, as an effective measure to protect patient privacy and prevent identity theft. Many Hospitals and Healthcare institutions are turning towards Vascular Biometrics which complements the biometric recognition with hygiene and improved accuracy. In this paper, a multimodal hand vein system and a multibiometric fingerprint-hand vein biometric system are proposed. The multimodal hand vein system is a non-invasive, contactless and fast system, which uses two different feature sets extracted from each hand vein image. The multibiometric system captures both the fingerprint as well as the hand vein of the patient and hence offers even more improved performance though the speed and the cost of the system as well as the hygiene are reduced. We have used the Euclidean classifier to calculate the performance rates namely the False Rejection Rate (FRR) and False Acceptance Rate (FAR) of the Vein System and the Fingerprint-Vein System. We have performed this analysis using a volunteer crew of 74 persons. The FRR and FAR were 0.46% and 0.7% in the former case and 0% and 0.01% in the latter case respectively. The multimodal or the multibiometric system could be used based of the Hospital’s requirements.

[1]  Xiao Han,et al.  Multiscale Feature Extraction of Finger-Vein Patterns Based on Curvelets and Local Interconnection Structure Neural Network , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[2]  Eduard Sojka A New and Efficient Algorithm for Detecting the Corners in Digital Images , 2002, DAGM-Symposium.

[3]  T. Tanaka,et al.  Biometric authentication by hand vein patterns , 2004, SICE 2004 Annual Conference.

[4]  Lingyu Wang,et al.  Near- and Far- Infrared Imaging for Vein Pattern Biometrics , 2006, 2006 IEEE International Conference on Video and Signal Based Surveillance.

[5]  Mount Lawley Thermographic Imaging of the Subcutaneous Vascular Network of the Back of the Hand for Biometric Identification , 1995 .

[6]  Chih-Lung Lin,et al.  Biometric verification using thermal images of palm-dorsa vein patterns , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Soo-Won Kim,et al.  An biometric identification system by extracting hand vein patterns , 2001 .

[8]  Anil K. Jain,et al.  Multibiometric systems: fusion strategies and template security , 2008 .

[9]  Kejun Wang,et al.  A study of hand vein recognition method , 2005, IEEE International Conference Mechatronics and Automation, 2005.

[10]  Yan Zhang,et al.  Hand Vein Recognition Based on Multi Supplemental Features of Multi-Classifier Fusion Decision , 2006, 2006 International Conference on Mechatronics and Automation.

[11]  Anil K. Jain,et al.  Fingerprint-Based Recognition , 2007, Technometrics.

[12]  Lingyu Wang,et al.  A Thermal Hand Vein Pattern Verification System , 2005, ICAPR.

[13]  Teddy Ko Multimodal biometric identification for large user population using fingerprint, face and iris recognition , 2005, 34th Applied Imagery and Pattern Recognition Workshop (AIPR'05).

[14]  Naoto Miura,et al.  Feature Extraction of Finger-vein Patterns Based on Repeated Line Tracking and Its Application to Personal Identification , 2022 .

[15]  Ajay Kumar,et al.  Personal Authentication Using Hand Vein Triangulation and Knuckle Shape , 2009, IEEE Transactions on Image Processing.

[16]  Sharath Pankanti,et al.  Biometrics: a tool for information security , 2006, IEEE Transactions on Information Forensics and Security.

[17]  Akshay Girdhar,et al.  Fingerprint Verification System Using Minutiae Extraction Technique , 2008 .

[18]  Arun Ross,et al.  Biometric template selection and update: a case study in fingerprints , 2004, Pattern Recognit..

[19]  Ahmed M. Badawi,et al.  Biometric Authentication Using Fast Correlation of Near Infrared Hand Vein Patterns , 2008 .