Open set recognition (OSR) in radio frequency fingerprint (RFF) is critical for securing Internet of Things (IoT) systems, where previously unseen or malicious devices may attempt unauthorized access. A widely used approach treats all unknown devices as a single additional class and assumes that they will produce low confidence scores during inference. However, due to the inherently subtle and highly similar RFF features across devices, this assumption often fails, leading to high false acceptance rates. To address this challenge, we propose a novel framework, called Multiple Unknown Classes Generation (MUCG), which replaces the single-class modeling of unknowns with a more expressive structure that simulates multiple distinct unknown classes. MUCG employs a conditional generative adversarial network (cGAN) guided by ideal signal priors to produce diverse and realistic unknown samples. Furthermore, we introduce a soft label perturbation (SLP) strategy that blends label semantics using Feature-wise Linear Modulation (FiLM), encouraging the generator to embed richer feature variations. Experiments on three public IoT datasets demonstrate that MUCG consistently outperforms state-of-the-art (SOTA) methods in OSR tasks, achieving superior accuracy and robustness under varying signal conditions.

Fingerprints, due to their uniqueness and stability, have become the most widely used biometric feature. Automated Fingerprint Recognition Systems (AFRS) have been applied in various scenarios for identity verification and access control. However, these systems have long faced serious threats from presentation attacks (PA), posing potential risks of privacy breaches and financial loss. Optical Coherence Tomography (OCT), as a non-invasive imaging technology, aligns with the demand for more secure and stable fingerprint recognition methods. By integrating OCT into AFRS, fingerprint recognition can be extended from traditional 2D surface fingerprints to OCT fingerprints containing 3D fingertip information. The rich and difficult-to-replicate structural details encoded in OCT fingerprints offer a highly promising solution for fingerprint presentation attack detection (PAD). Currently, publicly available OCT fingerprint datasets remain limited, and those specifically designed for PAD research are even rarer. This scarcity of data has significantly constrained research in OCT-based fingerprint PAD. To address this gap, a dedicated database for OCT fingerprint anti-spoofing, referred to as the ZJUT-PAD, has been designed and released by our research team. This database consists of 175 Presentation Attack Instruments (PAIs) made from 10 different materials, covering 35 distinct types. Each PAI was captured five times using two different OCT devices, resulting in a total of 1,750 PA instances. This database serves as a critical evaluation platform for OCT fingerprint PAD research, enabling a comprehensive assessment of performance, generalization capability, and cross-device robustness of PAD methods.

Optical coherence tomography (OCT) is a non-invasive imaging technique capable of acquiring depth information up to 1-3mm beneath the skin surface, including the stratum corneum and viable epidermis regions. This technique allows for the reconstruction of internal and external fingerprint images from grayscale data. However, existing fingerprint extraction methods heavily rely on contour features and current 2D approaches overlook the spatial continuity of biometric features in OCT images. Therefore, this paper proposes a novel iterative algorithm for internal and external fingerprint extraction from OCT images. This algorithm incorporates the spatial continuity of OCT slice images and an iterative feature enhancement module during the prediction phase to improve segmentation continuity. Additionally, a soft label technique is employed to reduce contour dependence and mitigate interference from noise and anomaly interference. Qualitative and quantitative experiments demonstrate significant improvements in segmentation accuracy with higher fingerprint quality, validating the effectiveness of the proposed approach.

As essential biometric features, fingerprints have been widely utilized in various security domains. However, the performance of conventional Automated Fingerprint Identification Systems (AFISs) is limited by the quality of the external fingerprint (EF), particularly in cases involving damaged or deformed prints. Using the internal fingerprint (IF) acquired by Optical Coherence Tomography (OCT) to address these limitations has emerged as a promising method. IFs can compensate for and restore missing ridge pattern features in degraded EFs, thereby improving the overall recognition accuracy of AFIS. However, the reconstruction of IF was significantly constrained by speckle noise in OCT images, making the accurate extraction of finger tissue contours complex and computationally intensive. To improve the applicability of OCT fingerprint, this paper proposes a Neural Architecture Search (NAS)-based OCT fingertip internal contour regression network, denoted as CRM-NAS. The CRM-NAS employs a NAS-based internal feature extraction module (NAS-IEM) to adaptively optimize the network architecture and complexity with noisy OCT fingertip data, facilitating the effective capture of global internal contour features. Furthermore, an attention-based contour regression module (Att-CRM) is introduced to refine local contour details by leveraging multi-scale intermediate features extracted from different network layers and to enable the generation of continuous and accurate internal contours. Experimental results demonstrate that CRM-NAS not only outperforms existing methods in terms of contour extraction accuracy, fingerprint reconstruction quality, and verification performance, but also maintains a relatively compact parameter size.

The rapid expansion of the Internet of Things (IoT) has heightened the necessity for device identity authentication to ensure security. Radio frequency fingerprint identification (RFFI) has emerged as a promising solution for this purpose, which leverage unique signal distortions caused by hardware impairments to authenticate device identities. However, most RFFI methods operate under a closed-set assumption and usually mistakenly identify unknown devices from the open set as known devices. In this paper, we propose a Synthetic Feature Constrain for open-set Recognition (SFCR) method to maintain classification performance on known devices and identify unknowns. Specifically, we modify the nonlinear characteristics of known devices based on the power amplifier nonlinearity model of radio frequency fingerprints (RFF) to synthesize signals for unknown devices. Furthermore, we propose a synthetic feature constraint to calibrate the position of synthetic devices in the feature space, such that they lie between the feature centers of the collective synthetic and originating known devices. As synthetic devices represent only a subset of the real unknown devices, we also introduce a calibration method for the prediction results. Experiments on a publicly available LoRa device dataset have validated the effectiveness of our approach. The code is released on github.com/wzyxwqx/SFCR.

External fingerprints (EFs) based only on epidermal information are vulnerable to spoofing attacks and non-ideal skin conditions. To solve such shortcomings, internal fingerprints (IFs) collected using optical coherence tomography (OCT) have been proposed and widely researched. However, the development of IF is limited by the lack of in-depth researches on the IF and the EF-IF interoperability, which is partially caused by the lack of public OCT database. The obvious gap in the applications of EF and IF recognition motivated us to design and publish a comprehensive fingerprint database containing both traditional EFs and OCT IFs, denoted as ZJUT-EIFD. To the best of our knowledge, ZJUT-EIFD is the first public database that combines OCT and total internal reflection (TIR) via synchronous acquisition, with 399 different fingers from 60 subjects. In this article, the composition of the database, the quality of EFs and IFs, and the verification performance of different types of fingerprints were detailed. In addition, potential application directions of ZJUT-EIFD were demonstrated. ZJUT-EIFD can serve benchmarks and interoperability tests for EF-IF research, which will promote the research and development of EF and IF.

Anti-spoofing ability is vital for fingerprint identification systems. Conventional fingerprint scanning devices can only obtain information from the fingertip surfaces, and their performance is susceptible to skin conditions and presentation attacks (PAs). However, optical coherence tomography (OCT) can scan subcutaneous tissue and obtain 3D fingerprint structures, naturally enhancing its PA detection (PAD) ability from the perspective of hardware. Existing unsupervised PAD methods are based on image reconstruction. However, the reconstruction error is easily affected by OCT noise and the rich details of OCT images. Therefore we propose feature-based reconstruction to alleviate this problem, called the prototype-guided autoencoder. The model consists of a memory module and a denoising autoencoder without the requirement of PA fingerprints. As only bona fide fingerprints are available during the training phase, the memory module contains the prototype features of the bona fide fingerprints. During the inference phase, as the prototype memory module is frozen, the reconstructed representation of the bona fide input is close to the bona fide fingerprint features. Calculating the distance between the original features and the prototype reconstructed representation of the sample can achieve PAD. To obtain a better decision making boundary, we propose a representation consistency constraint, which reduces the bona fide representation reconstruction distance closer, so that it is easier to differentiate between fingerprints and PAs.

Presentation attack detection (PAD) is a critical component of automated fingerprint recognition systems (AFRSs). However, existing PAD technologies based on optical coherence tomography (OCT) mainly rely on local information, ignoring the global continuity and correlation of physiological structures. Furthermore, the lack of appropriate presentation attack instruments (PAIs) that cater to the unique OCT characteristics leads to the insufficient evaluation of PAD. The identification features, including external fingerprint (EF), internal fingerprint (IF), and subcutaneous sweat pore (SSP), provide valuable information about the intrinsic connections of physiological structures. Such intrinsic connections hold potential clues for PAD. Building upon this premise, this paper proposed a novel PAD method based on three OCT hand-crafted features: EF-IF self-matching score (SMS), SSP number (SN), and SSP coincidence rate (SCR). These simple yet effective PAD features offer a more precise and detailed description of the internal physiological structure, enabling accurate distinction between presentation attack (PA) and bona-fide. The proposed method achieves a 4% Equal Error Rate (EER), significantly outperforming other existing PAD methods. Additionally, the cross-device experiment demonstrates the generalization capability of the proposed method on both our dataset and the public OCT dataset.