Biometric Verification Using Thermal Images of Palm-Dorsa Vein Patterns

Abstract

A novel approach to personal verification using the thermal images of palm-dorsa vein patterns is presented in this paper. The characteristics of the proposed method are that no prior knowledge about the objects is necessary and the parameters can be set automatically. In our work, an infrared (IR) camera is adopted as the input device to capture the thermal images of the palm-dorsa. In the proposed approach, two of the finger webs are automatically selected as the datum points to define the region of interest (ROI) on the thermal images. Within each ROI, feature points of the vein patterns (FPVPs) are extracted by modifying the basic tool of watershed transformation based on the properties of thermal images. According to the heat conduction law (the Fourier law), multiple features can be extracted from each FPVP for verification. Multiresolution representations of images with FPVPs are obtained using multiple multiresolution filters (MRFs) that extract the dominant points by filtering miscellaneous features for each FPVP. A hierarchical integrating function is then applied to integrate multiple features and multiresolution representations. The former is integrated by an inter-to-intra personal variation ratio and the latter is integrated by a positive Boolean function. We also introduce a logical and reasonable method to select a trained threshold for verification. Experiments were conducted using the thermal images of palm-dorsas and the results are satisfactory with an acceptable accuracy rate (FRR:2.3% and FAR:2.3%). The experimental results demonstrate that our proposed approach is valid and effective for vein-pattern verification.

Personal Authentication Using Finger Knuckle Surface

“…The hand-based biometrics has attracted lot of attention and personal identification using palmprint [1]- [2], [4]- [6], [14]- [15] hand-geometry, [7]- [8], 3D finger-geometry [9]- [10], and hand vein [11]- [13] have been proposed in the literature. The earlier approaches achieved the results by constraining the position and posture of hands by using pegs [2], [8], [11].…”

Hand vein biometry based on geometry and appearance methods

“…In a dataset of 100 persons, Hausdorff, LEM and Gabor based methods achieved accuracies of 58%, 66% and 80%, respectively. C.-L. Lin et al [13] presented person verification results using hand dorsal images acquired from IR images in the 3.4 -5 µm band. Their approach is based on the combination of multiresolution images obtained from the pre-processed thermal vein images.…”

Hand Vein Recognition with Single-layer Feature Learning Model

“…Meeting all the requirements of biometric identification like other personal traits [3], vein recognition is also characterized with high convenience in image acquisition and feature representation which results in wide investigation on vein information research in hand covering palm vein [4][5][6], dorsal vein [7] [8] [9] and finger vein [10]. And the entire framework for the three kinds of recognition is more or less same, which covers vein image preprocessing, vein image feature extraction and representation, classifier design and vein recognition [11].…”