VID: Human identification through vein patterns captured from commodity depth cameras

• Published in: IET biometrics Vol. 10; no. 2; pp. 142 - 162
• Main Authors: Shah, Syed W., Kanhere, Salil S., Zhang, Jin, Yao, Lina
• Format: Journal Article
• Language: English
• Published: Stevenage John Wiley & Sons, Inc 01.03.2021; Wiley
• Subjects: Accuracy; Biometrics; biometrics (access control); Cameras; Commodities; Deep learning; feature extraction; Identification; image classification; image recognition; Infrared imagery; Intrusion; learning (artificial intelligence); Machine learning; Smartphones; Veins & arteries
• ISSN: 2047-4938; 2047-4946
• DOI: 10.1049/bme2.12009

Herein, a human identification system for smart spaces called Vein‐ID (referred to as VID) is presented, which leverage the uniqueness of vein patterns embedded in dorsum of an individual's hand. VID extracts vein patterns using the depth information and infrared (IR) images, both obtained from a commodity depth camera. Two deep learning models (CNN and Stacked‐Autoencoders) are presented for precisely identifying a target individual from a set of N enrolled users. VID also incorporates a strategy for identifying an intruder—that is a person whose vein patterns are not included in the set of enrolled individuals. The performance of VID by collecting a comprehensive data set of approximately 17,500 images from 35 subjects is evaluated. The tests reveal that VID can identify an individual with an average accuracy of over 99% from a group of up to 35 individuals. It is demonstrated that VID can detect intruders with an average accuracy of about 96%. The execution time for training and testing the two deep learning models on different hardware platforms is also investigated and the differences are reported.