Development of mobile-based hand vein biometrics for global health patient identification

For many health services in developing countries, patient identification is a fundamental need. In countries where no standard form of identification is available, this problem is exacerbated by a lack of literacy and also frequent errors in spelling and consistency. To address this need, we implemented two low-cost hand vein scanner devices for use with mobile devices. The first scanner device employs the internal camera of the an Android smart phone along with a rechargeable infrared light (850nm) and an external optical filter; and the second scanner device employs a low-cost webcam, with integrated LEDs (940nm) and optical filter, which is powered directly from the Android tablet. A single mobile app was developed for use with both scanner devices with the ability to adjust scanner settings, capture hand palm images, and annotate patient data. As an initial test of our scanner designs, we collected hand scans from 51 university students aged 18-34 using an IRB-approved protocol, and data was processed using a 2D-PCA biometric algorithm implemented on a PC using MATLAB software. Using the standard FAR-FRR curve for biometric analysis, we were able to achieve an Equivalent Error Rate (EER) of 6.3% for the phone camera scanner, and 4.2% for the webcam scanner design. These results compare favorably with other published biometrics studies and demonstrate the potential of low-cost biometric devices that can be integrated with mobile phones and tablets.