Finger Vein Recognition Based on Local Directional Code

Abstract

Finger vein patterns are considered as one of the most promising biometric authentication methods for its security and convenience. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. As an improperly segmented network may degrade the recognition accuracy, binary pattern based methods are proposed, such as Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Line Binary Pattern (LLBP). However, the rich directional information hidden in the finger vein pattern has not been fully exploited by the existing local patterns. Inspired by the Webber Local Descriptor (WLD), this paper represents a new direction based local descriptor called Local Directional Code (LDC) and applies it to finger vein recognition. In LDC, the local gradient orientation information is coded as an octonary decimal number. Experimental results show that the proposed method using LDC achieves better performance than methods using LLBP.

Multi-feature Fusion Using SIFT and LEBP for Finger Vein Recognition

“…The first step to get LdBP feature is calculating the LDC value. It uses the difference of the neighbours of a pixel as the two components and of the local direction [18]. The equation(7) and equation(8) is followed to obtain and value.…”

“…The result of this difference is not influenced by varying illumination conditions. Some of other methods are proposed to improve the performance of LBP features, such as Local derivative Pattern (LDP) [16], Local Line Binary Pattern (LLBP) [17], and Local Directional Code (LDC) [18]. The newer TELKOMNIKA ISSN: 1693-6930 …”

Singular value decomposition based minutiae matching method for finger vein recognition

“…In ROI-based methods [16][17][18][19], the features are extracted from the whole ROI without finger vein network segmentation. Although promising experimental results are reported in [16][17][18][19]; in practice, these ROI-based methods may suffer from some limitations.…”

Finger Vein Recognition using Rotated Wavelet Filters

“…These original images are transformed into 8-bit gray scale image and Sobel edge detection operator is used for detecting the edges of finger from a finger vein image. Then the width and height of finger image is calculated using the maximum and minimum abscissa values of the finger profile [9]. Then the ROI of finger vein image is extracted based on these abscissa values and its size is normalized to 96x64 pixels using the bilinear interpolation [9].…”