Abstract
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.
Highlights
In recent years, biometric recognition has been a significant field in the security world that has witnessed an exponential increase in the integration of identification/authentication systems in daily life: access control, online payments, bank account access, and device unlocking
3) Brief demonstration of the process to be followed, as it is shown in Fig. 4, to position the wrist correctly according to TGS-CVBR R . 4) An operator takes one capture when the user’s wrist is placed correctly advising the user if the wrist is placed in an extremely wrong way: too far/near from the camera or with an incorrect orientation
EXPERIMENTS AND RESULTS To obtain the biometric and the computing time performance of the proposed system, the software algorithms presented in this work, TGS-CVBR R and PIS-CVBR R, have been faced with the 2400-images dataset collected
Summary
Biometric recognition has been a significant field in the security world that has witnessed an exponential increase in the integration of identification/authentication systems in daily life: access control, online payments, bank account access, and device unlocking. Hygiene is another important concern behind this constant and impressive growth of biometric-based systems, especially multiuser systems (e.g., access control). For this purpose, noncontact systems are designed. There are numerous contactless biometric modalities: facial, voice, iris, gait, vascular, and contactless fingerprint. In previous research [1], paying attention to vascular recognition modality and the current patents of palm vein (Fujitsu c PalmSecureTM, US 2005/0148876 A1 [2])
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