Compared with single biometric recognition, multimodal biometric recognition based on fingerprint and finger vein has been widely considered because of its convenient sample collection, high security and accurate recognition. However, according to our investigation, there is no public dataset of fingerprint and finger vein collected at the same time. The existing work uses fingerprint datasets and finger vein datasets from different sources for research, besides the researchers data from building their own equipment, which lacks consideration of practical applications. This is not conducive to the promotion of multibiometric technology based on finger. To promote research on multimodal biometric recognition based on fingerprint and finger vein, we design a finger collection device and introduce a new dataset, NUPT-FPV. It is the first public dataset to collect fingerprint and finger vein simultaneously in real-world applications. NUPT-FPV obtained 840 finger information from 140 volunteers, each finger was collected 20 times (collected in two sessions), and 33600 fingerprint and finger vein images were obtained. In addition, we propose a novel multimodal fusion method based on a convolutional neural network as a benchmark. Extensive experiments were conducted to verify the necessity of our dataset. Through the released dataset and benchmark, we hope to further promote the development of multimodal biometrics based on fingerprint and finger vein.

Recently, deep learning (DL) has achieved great success in biometric recognition. The application of DL has also led to a high demand for biometric data. However, as people attach more importance to privacy protection, biometric data have become increasingly difficult to obtain and access, leading to significant limitations in the development and application of DL-based biometric recognition. Federated learning (FL), a distributed learning technique with privacy protection, provides a potential solution to this problem. Several researchers have attempted to integrate FL into biometric recognition. These studies have shown that the introduction of FL not only solves the conflict between privacy and accessibility of biometric data but also improves the accuracy and generalizability of local recognition systems. Therefore, the combination of FL and biometric recognition techniques has become a new research hotspot. In this survey, we comprehensively review the latest advances regarding the application of FL to biometric recognition, biometric presentation attack detection and the related fields to provide new researchers with a quick and systematic overview of this emerging cross-disciplinary field. This paper also summarizes the future opportunities and challenges of this field. To our knowledge, this is the first survey that systematically organizes and analyses federated biometric recognition and related fields to provide suggestions and references for future research.

In current mainstream radar-based personnel identification technologies, the identification process typically relies on detecting the Doppler effect or the intensity of radar reflection signals. However, this method encounters limitations when there are multiple people within the radar detection area, as it cannot precisely locate each person in the real space. Moreover, when these signals are directly input into neural networks for learning, the networks tend to capture macroscopic information, such as body reflections and velocity, while overlooking detailed information crucial for identification, such as body posture and gait. To overcome this challenge, this study proposes a real-time through-wall multiperson identification system based on MIMO radar, named RTMP-ID. This system employs a global-local dual-branch structure to learn fine-grained identity information. The local branch focuses on introducing a radar-based human posture estimation network, aiming to accurately extract sequences of human postures. Based on these sequences, an identity feature extraction model is constructed to derive individual identity information from the postures. Meanwhile, the global branch integrates the posture information obtained from the local branch in a feedback manner, further accurately extracting the target reflection regions for feature extraction. By fusing the features extracted by both branches, the system achieves accurate identification of individuals. Our research results emphasized the critical importance of introducing 3-D pose sequences to enhance the robustness and accuracy of multitarget person identification. We conducted experiments across three scenarios and achieved a maximum recognition rate of 97.4%, even in the presence of stationary individuals.

Finger vein recognition methods, as emerging biometric technologies, have attracted increasing attention in identity verification due to their high accuracy and live detection capabilities. However, as privacy protection awareness increases, traditional centralized finger vein recognition algorithms face privacy and security issues. Federated learning, a distributed training method that protects data privacy without sharing data across endpoints, is gradually being promoted and applied. Nevertheless, its performance is severely limited by heterogeneity among datasets. To address these issues, this paper proposes a dual-decoupling personalized federated learning framework for finger vein recognition (DDP-FedFV). The DDP-FedFV method combines generalization and personalization. In the first stage, the DDP-FedFV method implements a dual-decoupling mechanism involving model and feature decoupling to optimize feature representations and enhance the generalizability of the global model. In the second stage, the DDP-FedFV method implements a personalized weight aggregation method, federated personalization weight ratio reduction (FedPWRR), to optimize the parameter aggregation process based on data distribution information, thereby enhancing the personalization of the client models. To evaluate the performance of the DDP-FedFV method, theoretical analyses and experiments were conducted based on six public finger vein datasets. The experimental results indicate that the proposed algorithm outperforms centralized training models without increasing communication costs or privacy leakage risks.

The deep learning-based finger vein recognition method has achieved excellent recognition performance in local tasks. However, in distributed scenarios where the method is deployed on terminal devices, the recognition performance experiences a significant decline for new users who have not been included in the model’s training set. One feasible solution is to equip the model with the capability of continual learning. Nevertheless, due to the increasing concerns over data privacy issues and legal restrictions, it becomes challenging for the recognition system to access vein images from different terminal devices to continuously improve the recognition model. In this work, to mitigate the conflict between model learning and data privacy, we propose a finger vein recognition framework based on federated learning, called FVRFL, which gives the recognition model sustainable learning ability in a privacy-aware manner. Furthermore, to tackle the challenge of user diversity in finger vein recognition terminals, we propose a modular optimization strategy. This strategy optimizes personalized models for different terminals and designs target functions for distinct modules, catering to the high-performance requirements of terminal users for the recognition system. Extensive experiments on public datasets demonstrate that the proposed method not only empowers the recognition model with the ability of continual learning, improving the performance of the recognition system on terminal devices, but also effectively protects the privacy of user vein templates.