The University of Auckland

Project #27: Developing a high resolution palm vein image scanner using near infrared camera for applications in Biometric Systems



Palm vein topography is a pattern recognition problem. Unlike other biometrics such as fingerprints or face recognition, the palm vein scanner works by capturing the images of the vein patterns that are beneath the skin of the palm. Thus, palm vein based biometrics is more secure than fingerprints and face recognition.

Palm vein recognition technology is secure because the authentication data exists inside the body and is therefore very difficult to forge. It is also highly accurate — in testing using 140,000 palm profiles of 70,000 individuals, it had a false acceptance rate of less than 0.00008% and a false rejection rate of 0.01%, as reported by Fujitsu Company. Fujitsu has developed a contactless palm vein pattern authentication technology that uses vascular patterns as personal identification data.

The process of palm vein recognition can be divided into several stages: image acquisition, pre-processing, feature extraction, matching and decision making. This project focuses on the image acquisition stage only. 

Palm veins are located beneath the skin and they are not visible in the visible light spectrum so the common photographic cameras cannot capture them. However, they can be captured within the near-infrared (NIR) spectrum (wavelength of 760-780 nm). The objective of this project is to develop a palm vein scanner using an infrared modified Digital Single Lens Camera (DSLR) with IR LEDs and IR bandpass filters, which can detect and capture high-resolution images of the vein patterns beneath the palm skin. It is a challenging problem where students need to build robust hardware and enhance the images obtained from the hardware to extract the vein patterns using image processing techniques. 

Interested students must see the supervisor before bidding for the project.



1.      Development of palm vein scanner setup using NIR (near infrared) Leds, IR (Infrared) Bandpass filter and IR modified Digital Single Lens Reflex Camera (DSLR).

2.     Application of Image Processing methods and Machine learning algorithms to perform pre-processing on the obtained images to enhance the vein network. 


Students with interest and experience in electronic and embedded systems design. Image processing knowledge is advantegeous. Interested students must see the supervisor before bidding for the project.







Lab allocations have not been finalised