1.1 Introduction
1.2 Device Design
1.3 System Implementation
1.3.1 ROI Extraction
1.3.1.1 Hyperspectral Palmprint ROI Extraction
1.3.1.2 Hyperspectral Dorsal Hand Vein ROI Extraction
1.3.2 Feature Extraction
1.3.3 Feature Fusion and Matching
1.4 Experiments
1.4.1 Multimodal Hyperspectral Palmprint and Dorsal Hand Vein Dataset
1.4.2 Optimal Pattern and Band Selection
1.4.3 Multimodal Identification
1.4.4 Multimodal Verification
1.4.5 Computational Complexity Analysis
1.5 Conclusions
Acknowledgements
References
Biometric recognition system has been widely used in the construction of a smart society. Many types of biometric systems, including face, iris, palmprint, palm vein, dorsal hand vein, and fingerprint, currently exist in security authentication. Palmprint recognition system is a kind of reliable authentication technology, due to the fact that palmprint has stable and rich characteristics, such as textures, local orientation features, and lines. In addition, a palmprint is user-friendly and cannot be easily captured by a hidden camera device without cooperation from the users. However, palmprint images captured using a conventional camera cannot be used in liveness detection. Palm vein is a good remedy for the weakness of palmprint acquired using a near-infrared (NIR) camera. The vein pattern is the vessel network underneath human skin. It can successfully protect against spoofing attacks and impersonation. Similar to palm vein, dorsal hand vein also has stable vein structures that do not change with age. Besides vein networks, some related characteristics to palmprint such as textures and local direction features can also be acquired.
Up to now, palmprint and dorsal hand vein-based recognition methods have achieved competitive performances in the literature. Huang et al. [1] put forward a method for robust principal line detection from the palmprint image, even if the image contained long wrinkles. Guo et al. [2] presented a binary palmprint direction encoding schedule for multiple orientation representation. Sun et al. [3] presented a framework to achieve three orthogonal line ordinal codes. Zhao et al. [4] constructed a deep neural network for palmprint feature extraction, where a convolutional neural network (CNN)-stack was constructed for hyperspectral palmprint recognition. Jia et al. presented palmprint-oriented lines in [5]. Khan et al. [6] applied the principle component analysis (PCA) to achieve a low-dimensionality feature in dorsal hand vein recognition. Khan et al. [7] obtained a low-dimensionality feature representation with Cholesky decomposition in dorsal hand vein recognition. Lee et al. [8] encoded multiple orientations using an adaptive two-dimensional (2D) Gabor filter in dorsal hand vein feature extraction.
The palmprint and dorsal hand vein recognition is usually carried out by conventional and deep learning-based methods. The conventional methods need to design a filter to extract the corresponding feature, i.e., local direction, local line, principal line, and texture. These hand-crafted algorithms usually require rich prior knowledge based on the specific application scenario. PalmCode [9] encoded palmprint features on a fixed direction by using a Gabor filter. Competitive code [10] extracted the dominant direction feature by using six Gabor filters. Xu et al. [11] encoded a competitive code aiming to achieve the accurate palmprint dominant ...