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.
Highlights
There has been much interest in biometric authentication for security purposes
The finger vein image captured by the device used in this paper, which is shown in Figure 5, is a 24-bit color image with a size of 320 × 240
Extraction of robust features from finger vein images is an important issue in finger vein-based biometric authentication
Summary
There has been much interest in biometric authentication for security purposes. Biometrics or biometric authentication [1,2] refers to automated methods of recognizing a person using behavioral or physiological features, such as, faces [3], irises [4], gaits [5], fingerprints [6,7], veins, etc. Sensors 2012, 12 features are unique characteristics to an individual which is convenient and more secure than traditional authentication methods. Biometric recognition is more reliable than token-based verification methods (keys or ID cards) and knowledge-based methods (passwords or PINs) while attaining higher efficiency and offering a better user experience. Personal verification based on biometric technology is widely used in door access control, security systems and forensics. Face recognition is susceptible to illumination changes and rotations. The conditions of a finger such as dryness or sweat can prevent a clear pattern from being obtained
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