Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma

Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique’s impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins.

Non-Contact Heart Rate Detection Based on Hand Vein Transillumination Imaging

Vein recognition technology identifies human vein characteristics under near-infrared light and compares it with stored vein information for personal identification. Although this has high anti-counterfeiting performance, it is possible to fabricate artificial hands that simulate vein characteristics to deceive the identity authentication system. In view of this potential deficiency, we introduced heart rate information to vein authentication, a means of living body detection, which can further improve the anti-counterfeiting effect of vein authentication. A hand vein transillumination imaging experiment was designed to prove its effectiveness. In the proposed method, a near-infrared light source is used to transilluminate the hand, and the transillumination images are collected by a common camera. Then, the region of interest is selected for gray-scale image processing, the feature value of each frame is extracted by superimposing and averaging the images, and then the one-dimensional pulse wave is drawn. Furthermore, the baseline drift phenomenon is filtered by morphological methods, and the maximum percentage frequency is determined by Fast Fourier Transform, that is, the pulse wave frequency. The heart rate value is then calculated, and finally, the stability of the heart rate detection result is evaluated. The experiment shows that the method produces accurate and stable results, demonstrating that it can provide living information (heart rate value) for vein authentication, which has great application prospects and development opportunities in security systems.