Local discriminant coding based convolutional feature representation for multimodal finger recognition

TL;DR: In this paper, a review of the recent research landscape in biometric finger vein recognition systems is presented, focusing on manuscripts related to keywords "Finger Vein Authentication System", "Anti-spoofing or Presentation Attack Detection", "Multimodal Biometric Finger Vein authentication", and their variations in four main digital research libraries such as IEEE Xplore, Springer, ACM, and Science Direct.

TL;DR: A finger collection device is designed and a novel multimodal fusion method based on a convolutional neural network as a benchmark is proposed, which is the first public dataset to collect fingerprint and finger vein simultaneously in real-world applications.

TL;DR: Based on compressive sensing and fractional-order simplest memristive chaotic system, this paper proposed an image compression and encryption scheme, which compresses the image twice to fully reduce the storage cost, and scrambles the pixel matrix twice through block scrambling and zigzag transformation, and then uses chaotic pseudo-random sequence and GF (17) domain diffusion image matrix to obtain the final cipher image.

TL;DR: This article delves into ViTs and proposes a novel model, FV Transformer (FVT), for FV authentication, which outperforms several baseline Transformer models and achieves competitive performance when compared with the state-of-the-art (SOTA) FV Authentication methods.

TL;DR: A prototype biometrics system which integrates faces and fingerprints is developed which overcomes the limitations of face recognition systems as well as fingerprint verification systems and operates in the identification mode with an admissible response time.

TL;DR: To robustly extract the precise details of the depicted veins, a method of calculating local maximum curvatures in cross-sectional profiles of a vein image is developed that can extract the centerlines of the veins consistently without being affected by the fluctuations in vein width and brightness.

TL;DR: A novel orientation based scheme is proposed, in which three strategies, the modified finite Radon transform, enlarged training set and pixel to area matching, have been designed to further improve its performance.

TL;DR: The acquisition and content of a new homologous multimodal biometric database are presented and the database is available to research community through http://mla.sdu.edu.cn/sdumla-hmt.