Vein Biometric Recognition on a Smartphone

• Published in: IEEE access Vol. 8; pp. 104801 - 104813
• Main Authors: Garcia-Martin, Raul, Sanchez-Reillo, Raul
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biometrics; Biometrics (access control); biometrics on mobile devices; Cameras; contactless database; Electronic devices; Face recognition; Feature extraction; Fingerprints; Infrared cameras; Infrared imagery; near-infrared camera; SIFT® (scale-invariant feature transform); smartphone; Smartphones; Software; SURF®(speeded up robust features); Vein biometric recognition; Veins; Wrist; wrist vascular biometric recognition; Xiaomi© Mi 8; Xiaomi© Pocophone F1
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3000044

Human recognition on smartphone devices for unlocking, online payment, and bank account verification is one of the significant uses of biometrics. The exponential development and integration of this technology have been established since the introduction in 2013 of the fingerprint mounted sensor in the Apple iPhone 5s by Apple Inc.© (Motorola© Atrix was previously launched in 2011). Nowadays, in the commercial world, the main biometric variants integrated into mobile devices are fingerprint, facial, iris, and voice. In 2019, LG© Electronics announced the first mobile exhibiting vascular biometric recognition, integrated using the palm vein modality: LG© G8 ThinQ (hand ID). In this work, in an attempt to become the become the first research-embedded approach to smartphone vein identification, a novel wrist vascular biometric recognition is designed, implemented, and tested on the Xiaomi© Pocophone F1 and the Xiaomi© Mi 8 devices. The near-infrared camera mounted for facial recognition on these devices accounts for the hardware employed. Two software algorithms, TGS-CVBR® and PIS-CVBR®, are designed and applied to a database generation and the identification task, respectively. The database, named UC3M-Contactless Version 2 (UC3M-CV2), consists of 2400 contactless infrared images from both wrists of 50 different subjects (25 females and 25 males, 100 individual wrists in total), collected in two separate sessions with different environmental light environmental light conditions. The vein biometric recognition, using PIS-CVBR®, is based on the SIFT®, SURF®, and ORB algorithms. The results, discussed according to the ISO/IEC 19795-1:2019 standard, are promising and pave the way for contactless real-time-processing wrist recognition on smartphone devices.