Fast cross-correlation based wrist vein recognition algorithm with rotation and translation compensation

Abstract

Most of the research on vein biometrics addresses the problems of either palm or finger vein recognition with a considerably smaller emphasis on wrist vein modality. This paper paves the way to a better understanding of capabilities and challenges in the field of wrist vein verification. This is achieved by introducing and discussing a fully automatic crosscorrelation based wrist vein verification technique. Overcoming the limitations of ordinary cross-correlation, the proposed system is capable of compensating for scale, translation and rotation between vein patterns in a computationally efficient way. Introduced comparison algorithm requires only two cross-correlation operations to compensate for both translation and rotation, moreover the well known property of log-polar transformation of Fourier magnitudes is not involved in any form. To emphasize the veins, a two-layer Hessian-based vein enhancement approach with adaptive brightness normalization is introduced, improving the connectivity and the stability of extracted vein patterns. The experiments on the publicly available PUT Vein wrist database give promising results with FNMR of 3.75% for FMR ≈ 0.1%. In addition we make this research reproducible providing the source code and instructions to replicate all findings in this work.

Vein Biometric Recognition on a Smartphone

“…The most common preprocessing or intermediate biometric sample processing techniques, starting from monochromatic infrared images, are histogram equalization ( [4], [14], and [21]), and noise reduction using filters (2D Median and Gaussian filters, [14] and [22]). These steps are followed to isolate and enhance the visualization of the patterns described by the vascular tissues.…”

“…Then, for feature extraction, several techniques have been used: Dense Local Binary Pattern (D-LBP) in [21]; Maximum Curvature Points (MCP), Multi-scale match filter, Sparse Representation Classifier (SRC), LBP, Local Phase Quantization (LPQ), Histogram of Gradients (HOG), Steerable pyramids, Local Binary Patterns Variance (LBPV), and Log Gabor Filters in [4]; Binarization in [14] and Hessian matrix in [22]. Most of these methods, based on segmentation and biometric template comparison, are not robust enough against vein tissue orientation, scale, or even deformation [23].…”

Wrist Vascular Biometric Recognition Using a Portable Contactless System

“…Fast Cross-Correlation Based Wrist Vein Recognition Algorithm with Rotation and Translation Compensation [13] (2018) Dataset Name Singapore (NIR, Own) [9] UC3M (Own) [8] and Singapore [5] UC3M [8] and Singapore [9] PUT (Public) [6] PUT […”

State of the Art in Vascular Biometrics

“…Using vascular pattern-related feature extraction, [177] propose the fusion of left and right wrist data; a classical preprocessing cascade is used and binary images resulting from local and global thresholding are fused for each hand. A fast computation of cross-correlation comparison of binary vascular structures with shift compensation is derived in [186]. Another low-cost sensor device is proposed in [221].…”