Optical coherence tomography (OCT), as a non-destructive and high-resolution fingerprint acquisition technology, is robust against poor skin conditions and resistant to spoof attacks. It measures fingertip information on and beneath skin as 3D volume data, containing the surface fingerprint, internal fingerprint and sweat glands. Various methods have been proposed to extract internal fingerprints, which ignore the inter-slice dependence and often require manually selected parameters. In this paper, a modified U-Net that combines residual learning, bidirectional convolutional long short-term memory and hybrid dilated convolution (denoted as BCL-U Net) for OCT volume data segmentation and two fingerprint reconstruction approaches are proposed. To the best of our knowledge, it is the first time that simultaneous and automatic extraction is performed for surface fingerprint, internal fingerprint and sweat glands. The proposed BCL-U Net utilizes the spatial dependence in OCT volume data and deals with segmentation of objects with diverse sizes to achieve accurate extraction. Comparisons have been performed to demonstrate the advantages of the proposed method. A thorough evaluation of the recognition abilities of internal and surface fingerprints is conducted using a dataset significantly larger than previous studies. Four databases containing internal and surface fingerprints are generated from 1572 OCT volume data by the proposed method. The internal fingerprint matching experiment has achieved a lowest equal error rate (EER) of 0.95%. Mixed internal and surface fingerprint matching experiment is also performed and achieves an EER of 3.67%, verifying the consistency of the internal and surface fingerprints. The matching experiments for fingers under poor skin conditions show that internal fingerprints achieve an EER of 2.47% that is much lower than that of surface fingerprints, which proves the advantage of internal fingerprints and indicates the potential of the internal fingerprints to supplement or replace the surface fingerprints for some specific applications.

The research of external fingerprint collected by total internal reflection (TIR) has been carried out for decades and research of internal fingerprint of optical coherence tomography (OCT) has just begun. The internal fingerprint can be hardly affected by the finger surface status, due to its strong anti-interference and anti-spoofing ability, which can serve as a powerful supplement to external fingerprint. However, matching fingerprints acquired by different ways can lead to a drop in fingerprint recognition accuracy owing to the differences in fingerprint quality, distortions and detection areas. Whether the internal fingerprint can be used to replace the external fingerprint for direct identification has been hampered by lacking comparison tools to study potential correlation with each other. To study the connection between internal and external fingerprints, a synchronous acquisition system that achieves the same fingerprint area measurement at the same time is necessary. The integration of different optical paths of TIR and OCT as well as the consequent distortion correction of two totally different imaging ways are two challenging problems. This paper presents a fingerprint acquisition system that synchronously acquires the external fingerprint and internal fingerprint with TIR and OCT, respectively. By using a self-designed trapezoidal prism, optical paths of TIR and OCT were integrated. Furthermore, a distortion correction method based on a grid calibration plate is used to remove distortions caused by two imaging methods. The fingerprint quality statistics is illustrated in 264 sets of fingerprint datasets. Identification results show synchronous acquisition and distortion correction of proposed system is efficient.

The quinolone antibiotics represented by enrofloxacin (ENRO) are harmful to the ecological environment and human health due to illegal excessive use, resulting in increasing food residues and ENRO levels in the environment. To this end, we developed a MIPs–SERS method using surface-enhanced Raman spectroscopy (SERS) and molecularly imprinted polymers (MIPs) to detect ENRO in food matrices. Firstly, a layer of silver nanoparticles (Ag NPs) with the best SERS effect was synthesized on the surface of copper rods as the enhancing material by in situ reductions, and then MIPs targeting ENRO were prepared by the native polymerization reaction, and the MIPs containing template molecules wrapped on the surface of silver nanoparticle films (Ag NPs–MIPs) were obtained. Our results showed that the Ag NPs–MIPs could specifically identify ENRO from the complex environment. The minimum detection limit for ENRO was 0.25 ng/mL, and the characteristic peak intensity of ENRO was linearly correlated to the concentration with a linear range of 0.001~0.1 μg/mL. The experimental results showed that in comparison to other detection methods, the rapid detection of ENRO in food matrices using Ag NPs–MIPs as the substrate is reliable and offers a cost-effective, time-saving, highly selective, and sensitive method for detecting ENRO residues in real food samples.

External fingerprints captured by conventional scanners are easily affected by pollution or abrasion. Optical coherence tomography can capture stable internal fingerprints located inside fingertips but with comparable unstable qualities. External and internal fingerprints have advantages and disadvantages that can be used to complement each other. Registration is the premise of the fusion of these fingerprints. Multisensor differences between external and internal fingerprints, including image style differences and deformations, make the registration challenging. This paper presents a new multisensor fingerprint registration method to align external and internal fingerprints finely for the first time. The proposed method includes two stages. First, a cycle generative adversarial network with a structural constraint is designed to unify the image style of multisensor fingerprints. It can improve the similarity of fingerprints while preserve the valley ridge structures. Then, a two step registration with fast digital image correlation and outlier rejection is proposed to realize global to local registration. Comparisons have been performed to demonstrate the advantage of the proposed method in registration accuracy and matching performance. Fingerprint fusion results also verify that the registration and fusion of external and internal fingerprints are conducive to supplement fingerprint information.

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.