Hand-Dorsa Vein Recognition by Matching Local Features of Multisource Keypoints

Abstract

As an emerging biometric for people identification, the dorsal hand vein has received increasing attention in recent years due to the properties of being universal, unique, permanent, and contactless, and especially its simplicity of liveness detection and difficulty of forging. However, the dorsal hand vein is usually captured by near-infrared (NIR) sensors and the resulting image is of low contrast and shows a very sparse subcutaneous vascular network. Therefore, it does not offer sufficient distinctiveness in recognition particularly in the presence of large population. This paper proposes a novel approach to hand-dorsa vein recognition through matching local features of multiple sources. In contrast to current studies only concentrating on the hand vein network, we also make use of person dependent optical characteristics of the skin and subcutaneous tissue revealed by NIR hand-dorsa images and encode geometrical attributes of their landscapes, e.g., ridges, valleys, etc., through different quantities, such as cornerness and blobness, closely related to differential geometry. Specifically, the proposed method adopts an effective keypoint detection strategy to localize features on dorsal hand images, where the speciality of absorption and scattering of the entire dorsal hand is modeled as a combination of multiple (first-, second-, and third-) order gradients. These features comprehensively describe the discriminative clues of each dorsal hand. This method further robustly associates the corresponding keypoints between gallery and probe samples, and finally predicts the identity. Evaluated by extensive experiments, the proposed method achieves the best performance so far known on the North China University of Technology (NCUT) Part A dataset, showing its effectiveness. Additional results on NCUT Part B illustrate its generalization ability and robustness to low quality data.

A Dynamic Biometric Authentication Algorithm for Near-Infrared Palm Vascular Patterns

“…• Given (11) and (12) in the absence of cross matching (µ = 0) and of initial expansion terms (ν = 0), x ij (k) and y ij (k) should asymptotically converge to zero: since the iterations become x ij (k + 1) = λx ij (k) and y ij (k + 1) = λy ij (k), this happens provided that λ < 1, as in (14). • The expansion term initially augments x ij (resp.…”

“…Biometric systems can indeed automatically authenticate or identify subjects in a reliable and fast way and are therefore suitable to be used in a wide range of applications to face the risks of unauthorised logical or physical access and identity theft, as well as new threats such as terrorism or cybercrime [11].…”

State of the Art in Vascular Biometrics

“…[270] propose a fusion of multiple sets of SIFT key points which aims at reducing information redundancies and improving the discrimination power, respectively. Different types of key points are proposed to be used by [92], namely, based on Harris corner-ness measurement, Hessian blob-ness measurement and detection of curvature extrema by operating the DoG detector on a human vision inspired image representation (so-called oriented gradient maps).…”

Dorsal Hand Vein Pattern Analysis and Neural Networks for Biometric Authentication

“…Finite differences are used to approximate the equation (4), which lead to the discrete form of the anisotropic diffusion, which is characterized by an iterative procedure applied on the input palm image. The number of iterations corresponds to the t parameter from equation (4).…”