Internal Fingerprint Research Articles

Pressure-Insensitive Epidermal Thickness of Fingertip Skin for Optical Image Encryption.

In this study, an internal fingerprint-guided epidermal thickness of fingertip skin is proposed for optical image encryption based on optical coherence tomography (OCT) combined with U-Net architecture of a convolutional neural network (CNN). The epidermal thickness of fingertip skin is calculated by the distance between the upper and lower boundaries of the epidermal layer in cross-sectional optical coherence tomography (OCT) images, which is segmented using CNN, and the internal fingerprint at the epidermis-dermis junction (DEJ) is extracted based on the maximum intensity projection (MIP) algorithm. The experimental results indicate that the internal fingerprint-guided epidermal thickness is insensitive to pressure due to normal correlation coefficients and the encryption process between epidermal thickness maps of fingertip skin under different pressures. In addition, the result of the numerical simulation demonstrates the feasibility and security of the encryption scheme by structural similarity index matrix (SSIM) analysis between the original image and the recovered image with the correct and error keys decryption, respectively. The robustness is analyzed based on the SSIM value in three aspects: different pressures, noise attacks, and data loss. Key randomness is valid by the gray histograms, and the average correlation coefficients of adjacent pixelated values in three directions and the average entropy were calculated. This study suggests that the epidermal thickness of fingertip skin could be seen as important biometric information for information encryption.

ZJUT-EIFD: A Synchronously Collected External and Internal Fingerprint Database.

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.

Quality-Aware Fusion of Multisource Internal and External Fingerprints Under Multisensor Acquisition

Fingerprint is one of the most widely used biometric features. The researches on external fingerprint collected by total internal reflection and the internal fingerprint obtained by optical coherence tomography have been carried out extensively. Studies proved the consistency between them. The external fingerprint is susceptible to the fingertip status, whereas the internal fingerprint has strong anti-interference and anti-spoofing capabilities. They originate from different skin layers and are collected by multiple sensors. Given the corresponding advantages and disadvantages of external and internal fingerprints, their fusion can maximize effective information and improve fingerprint image quality; thus, this fusion is conducive to fingerprint identification. In this study, a fingerprint fusion method based on the quality-aware convolutional-sparsity-based morphological component analysis (CSMCA) is proposed. The proposed method realizes the fusion of multisource and multisensor fingerprints for the first time. Quality indexes, namely, spatial consistency, dryness, and humidity, are selected. Moreover, a simulation-based combination scheme is proposed for the pixel-level quality representation. The quality index is integrated with CSMCA for quality-aware fusion, which retains high-quality components and reduces low-quality areas. The experiments prove the superiority of the proposed method in quality scores and matching performances, indicating that internal fingerprints can amend external fingerprints. The matching experiments with either external or internal fingerprints show that the fused fingerprints can be compatible with the existing fingerprint databases. Our work can provide references and insights into identifying mutilated fingerprints in the future.

3D CNN-based fingerprint anti-spoofing through optical coherence tomography

Optical coherence tomography (OCT) is a noninvasive high-resolution imaging technology that can accurately acquire the internal characteristics of tissues within a few millimeters. Using OCT technology, the internal fingerprint structure, which is consistent with external fingerprints and sweat glands, can be collected, leading to high anti-spoofing capabilities. In this paper, an OCT fingerprint anti-spoofing method based on a 3D convolutional neural network (CNN) is proposed, considering the spatial continuity of 3D biometrics in fingertips. Experiments were conducted on self-built and public datasets to test the feasibility of the proposed anti-spoofing method. The anti-spoofing strategy using a 3D CNN achieved the best results compared with classic networks.

Methods and Applications of Fingertip Subcutaneous Biometrics Based on Optical Coherence Tomography

Degraded skin conditions and advanced forgery technologies have reduced the stability and security of identity authentication based on external biometrics of the fingertip, including fingerprints, sweat pores, etc. Biological studies indicate that these external biometrics have their subcutaneous origins. Following the successful application of optical coherence tomography (OCT), the fingertip subcutaneous biometrics such as internal fingerprints and sweat glands have been collected. While researchers have worked on different aspects on subcutaneous biometrics, there is a lack of review of the methods and applications to our best knowledge. A survey on every key step in the subcutaneous biometrics processing is conducted in this paper, including the OCT system design, the extraction of subcutaneous biometrics, the identification and anti-counterfeiting applications of new features. Discussion on the pros and cons of methods, and analysis on the research and application directions, are made in every step. The linkage and difference with the conventional fingerprints are illustrated. Fingertip subcutaneous biometrics will undoubtedly become a new trend in highly secure authentication. This paper intends to provide systematic references and insights of methods and applications and support the researchers for their works on fingertip subcutaneous biometrics.

End-to-End Surface and Internal Fingerprint Reconstruction From Optical Coherence Tomography Based on Contour Regression

Optical coherence tomography (OCT), as a non-destructive and high-resolution imaging technique, has been used to collect 3D fingertip data, which contains surface and internal fingerprints. Methods have been proposed for OCT fingerprint reconstruction. However, these methods have complex processing flow and are time consuming. In this paper, an end-to-end convolutional neural network based surface and internal fingerprint reconstruction method is proposed. A simple yet effective contour regression module is proposed and integrated in the network for direct estimation of contours of stratum corneum and viable epidermis junction from noisy OCT volume data, thus greatly simplify the processing flow. The proposed network further integrates multi-task learning with conventional segmentation task as auxiliary task and contour regression task as main task to facilitate the feature extraction and improve the robustness of the network. Depthwise separable convolution is adapted to a light-weight network for network computation complexity reduction. To the best of our knowledge, it is the first time that an end-to-end method is proposed for surface and internal fingerprint extraction from noisy OCT volume data. Experiments and comparisons are carried out in terms of contour estimation accuracy, fingerprint quality, fingerprint matching performance and computation efficiency. Compared with conventional method, the proposed method utilizes only 6% of original network parameters and 0.7% of original computation time, but achieves comparably results. Fingerprint by depth proves the accuracy and robustness of contour regression than pixel-wise layer segmentation. The proposed method is noise-insensitive, process-simple and time-efficient for OCT fingerprint reconstruction, which is significant for real time application in Automated Fingerprint Recognition Systems.

Anti-Spoofing Research of OCT Fingerprint Based on Attention Residual Network

Current optical coherence tomography (OCT) based fingerprint anti-counterfeiting methods are mainly through the extraction of sweat pores, subcutaneous sweat glands and internal fingerprints, which depend on the extraction accuracy and contain redundant anti-counterfeiting information. An OCT fingerprint anti-counterfeiting method based on the attention residual network is proposed, where only a small amount of data is required to identify the authenticity of the fingerprint. Firstly, the residual network is incorporated with the convolutional block attention module as well as the long and short skip connections to better focus the network attention on the subcutaneous tissue information. Secondly, random sampling is performed on the volume data of the discriminating finger, and the local feature patches are extracted. In the last, these local feature patches are classified by the attention residual network to realize the identification of the true and fake fingerprint. Experiments are carried out using the Keras framework and on the self-established true and fake fingerprint database. The proposed method obtains a higher anti-counterfeiting accuracy rate than other networks, which proves the superiority of the proposed method. The cross-device and cross-sample experiment on self-established database and Shenzhen University OCT fingerprint database has further verified the accuracy and robustness of the proposed method.

Automatic algorithm for the characterization of sweat ducts in a three-dimensional fingerprint.

In this study, an automatic algorithm has been presented based on a convolutional neural network (CNN) employing U-net. An ellipsoid and an ellipse were applied for approximation of a three-dimensional sweat duct and en face sweat pore at the different depths, respectively. The results demonstrated that the length and the diameter of the ellipsoid can be used to quantitatively describe the sweat ducts, which has a potential for estimating the frequency of resonance in millimeter (mm) wave and terahertz (THz) wave. In addition, projection-based sweat pores were extracted to overcome the effect that the diameters of en face sweat pores depend on the depth. Finally, the projection-based image of sweat pores was superposed with a maximum intensity projection (MIP)-based internal fingerprint to construct a hybrid internal fingerprint, which can be applied for identification recognition and information encryption.

Depth-independent internal fingerprint based on optical coherence tomography.

Optical coherence tomography (OCT) was used for imaging three-dimensional fingerprint to overcome the effects of different skin states and fake fingerprint. However, the OCT-based fingerprint features depend on the depth of fingertip skin which is still challenging for biometric recognition and encryption. In this work, we presented a new approach of maximum intensity projection (MIP) image of the epidermal-dermal junction (DEJ) to extract the internal fingerprint that is independent of the depth of fingertip skin. To begin with, the surface and DEJ were segmented based on the deep learning algorithm. Then the internal fingerprint was extracted by the MIP image of DEJ which has a more accurate structural similarity by quantitative analysis. The experimental results showed that internal fingerprint acquired by MIP of DEJ can be applied for scar-simulation fingertip and encryption since it is not sensitive to the states of surface skin and independent of the depth.

A New Approach to External and Internal Fingerprint Registration With Multisensor Difference Minimization

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.

Synchronous Fingerprint Acquisition System Based on Total Internal Reflection and Optical Coherence Tomography

The research of external fingerprint collected by total internal reflection (TIR) has been carried out for decades and the research of internal fingerprint collected by optical coherence tomography (OCT) has just begun. The internal fingerprint can be hardly affected by the finger surface status, due to its strong antiinterference and antispoofing ability, which can serve as a powerful supplement to external fingerprint. However, matching fingerprints acquired in different ways can lead to a drop in fingerprint recognition accuracy due 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 article 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 are illustrated in 264 sets of fingerprint data sets. Identification results show synchronous acquisition and distortion correction of the proposed system are efficient.

Surface and Internal Fingerprint Reconstruction From Optical Coherence Tomography Through Convolutional Neural Network

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 article, 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 gland. 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 a 2.47% EER of internal fingerprints 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.

Robust and high-security fingerprint recognition system using optical coherence tomography

Traditional fingerprint recognition systems are vulnerable to attacks, such as the use of artificial fingerprints, and poor performance will be achieved if the captured surface fingerprints are of low-quality. Developing high-security and robust fingerprint recognition systems is of increasing concern in modern society. The introduction of optical coherence tomography (OCT) for fingerprint imaging opens up a new research domain for fingerprint recognition due to its ability to capture the depth information of skin layers. This paper proposes a fingerprint recognition system based on OCT. The research first establishes a database with normal, worn-out, artificial and degraded fingerprints imaged by our custom-built, in-house OCT device. Then, we propose to reconstruct three layers of subsurface fingerprints by considering diverse skin layer information. For each of the subsurface fingerprints divided according to the physical structure of the fingertip, we propose a simple yet effective projection-based reconstruction method. Finally, a pixel-level fusion strategy based on the local image quality is proposed to the fuse the three levels of subsurface fingerprints for robust fingerprint recognition. In our experiments, we show that as much diverse fingerprint information can be retained as possible by the proposed subsurface fingerprint reconstruction method. We also demonstrate the effectiveness and efficiency of the proposed method by comparing it with existing state-of-the-art internal fingerprint reconstruction approaches. The robustness and high anti-spoofing ability of the proposed system is verified by comparing the matching performance evaluated on the established OCT-based database and another database with the same fingerprints imaged by a commercial optical sensor. The best EER and FMR100 evaluated on OCT-based fingerprints are 0.42% and 0.36%, respectively. The best EER and FMR100 evaluated on traditional 2D surface fingerprints are 8.05% and 18.18%, respectively, which shows the vast potential of the proposed system in current automated fingerprint recognition systems (AFRSs).

Acquisition and extraction of surface and internal fingerprints from optical coherence tomography through 3D fully convolutional network

Optical coherence tomography (OCT) is a non-destructive, high-resolution, and non-invasive imaging technique that has recently been applied to biometric field for three dimensional (3D) fingertip data acquisition. Fingertip volume data contains the epidermis and part of the dermis layers representing the surface fingerprint and internal fingerprint, respectively. As a 3D and under skin feature, internal fingerprint provides much higher anti-counterfeiting capability compared with traditional 2D fingerprint. Furthermore, it is not affected by fingertip conditions such as wet, worn out (old) and small scars/injuries. However, the extraction of surface and internal fingerprints is challenging due to the speckle noise and the low contrast of the data. In this paper, a spectral domain OCT system is constructed for fingertip volume data acquisition. A simultaneous and automatic surface and internal fingerprint extraction method with 3D fully convolutional network is proposed. The 3D fully convolutional network is adapted for the contour extraction of stratum corneum and papillary junction with the inter-slice spatial dependency reserved by three-dimensional convolution. Subsequently, surface and internal fingerprints are extracted. Experiments are performed to demonstrate the effectiveness of our proposed method both qualitatively and quantitatively. The internal and external fingerprints are also compared and discussed.

Compact and Mobile Full-Field Optical Coherence Tomography Sensor for Subsurface Fingerprint Imaging

Conventional fingerprint sensors that are deployed in real-life applications lack the ability to peer inside a finger beyond the external surface. Subsurface information can provide complimentary biometric characteristics associated with the finger. The subsurface fingerprints can also be employed when the quality of the external/surface fingerprints is affected. One of the most promising technologies for imaging below the surface of an external fingerprint is full-field optical coherent tomography (FF-OCT). However, the FF-OCT can be expensive and cumbersome, despite its proven ability for biometric use. In this paper, we describe the design and implementation of a compact, mobile and cost-effective FF-OCT sensor that is stable and easy to use. The newly designed sensor, being $30 \,\,cm \,\,\times \,\,30 \,\,cm \,\,\times \,\,10 \,\,cm$ in size, comprises of a dedicated silicon camera, stable Michelson interferometer and a bright Near-Infra-Red (NIR) light emitting diode. It enables recording of $1.7 \,\,cm \times 1.7 \,\,cm$ images of subsurface finger features, such as internal fingerprints and sweat ducts. We show the employability of the newly designed sensor for different applications. Specifically, we validate its usefulness by capturing subsurface fingerprints of 585 subjects leading to 3510 unique fingerprints. The resulting accuracy of 0.74% as Equal Error Rate ( $EER$ ) indicates the backward compatibility of the proposed sensor with the existing commercial off-the-shelf algorithms. Thanks to the large fingerprint database collected in this work we determined the most useful imaging depth for the fingerprint matching purposes to be around $100~\mu m$ . As an additional advantage, the sensor could be readily used in other applications with little or no modification, such as $in ~vivo$ skin imaging.

Multiscale skin imaging in vivo using optical coherence tomography

Optical coherence tomography (OCT) is a noninvasive high-resolution diagnostic imaging modality that plays an increasingly important role in dermatology. Diagnosis of skin diseases using OCT requires both cellular-level high resolution and large area skin coverage. In practice, however, there exists a trade-off between the achievable spatial resolutions and the transverse scanning range. In this study, we report a Micro-OCT (μOCT) system that is capable of providing three-dimensional images of the skin at multiple spatial scales with both cellular-level resolution (1–2 μm) mode and large area (∼ mm2) scanning mode. Specifically, in the cellular-level scanning mode, we achieve a transverse resolution of ∼1.5 μm and an axial resolution of 1.7 μm (n = 1.38) which enables the visualization of cellular-level skin microstructures. While in the large-area scanning mode, the system is capable of covering an en face imaging area reaching up to with a lateral resolution of ∼5.5 μm at a scanning speed of 60 K Alines/s. We experimentally verify the imaging capabilities of such a multiscale μOCT system including in vivo visualization of epidermal cells in the cellular-level scanning mode as well as the internal fingerprints and sweat gland ducts in the large area scanning mode. Micro-anatomical imaging at multiple spatial scales could provide comprehensive information of the skin that is valuable to disease diagnosis.

Fast subsurface fingerprint imaging with full-field optical coherence tomography system equipped with a silicon camera.

Images recorded below the surface of a finger can have more details and be of higher quality than the conventional surface fingerprint images. This is particularly true when the quality of the surface fingerprints is compromised by, for example, moisture or surface damage. However, there is an unmet need for an inexpensive fingerprint sensor that is able to acquire high-quality images deep below the surface in short time. To this end, we report on a cost-effective full-field optical coherent tomography system comprised of a silicon camera and a powerful near-infrared LED light source. The system, for example, is able to record 1.7 cm×1.7 cmen face images in 0.12s with the spatial sampling rate of 2116 dots per inch and the sensitivity of 93dB. We show that the system can be used to image internal fingerprints and sweat ducts with good contrast. Finally, to demonstrate its biometric performance, we acquired subsurface fingerprint images from 240 individual fingers and estimated the equal-error-rate to be ∼0.8%. The developed instrument could also be used in other en face deep-tissue imaging applications because of its high sensitivity, such as in vivo skin imaging.

Contrast Enhanced Subsurface Fingerprint Detection Using High-Speed Optical Coherence Tomography

Spectral-domain optical coherence tomography (SD-OCT) has been demonstrated to be a viable tool in forensic science for fingerprint detection, yet it still suffers from certain practical issues, e.g., the limited scanning speed and low image contrast. In this letter, we report a high-speed SD-OCT together with an image contrast enhancement mechanism for reliable subsurface fingerprint detection. The constructed SD-OCT system achieves a scanning rate up to 60k A-lines/s, and thus, both 3D volumetric images, reaching up to 20 mm $\times 20$ mm $\times 1.2$ mm, and en face internal fingerprint furrow pattern images could be obtained. Based upon the analyses of the papillary layer anatomical structures, the contrast enhancement technique not only suppresses image artifacts, but also is effective in detecting fingerprint spoofing. Experiments on healthy subjects have also been conducted to verify the system imaging capability and the effectiveness of the contrast-enhancement technique.

Live Acquisition of Internal Fingerprint With Automated Detection of Subsurface Layers Using OCT

Recently, optical coherence tomography (OCT) was introduced as a novel fingerprint scanning technique. This approach is resistant to fake fingerprint attacks, and is robust against poor fingertip conditions, i.e., wet or stained fingers. The method proceeds by scanning a fingertip in three dimensions and capturing a fingerprint from the subsurface layer. Although OCT has the potential to be widely used as a new standard in fingerprint scanning, it is hindered by its low scanning speed and the lack of computing power available for reproducing raw OCT data into images in real time; for instance, this process can take minutes to obtain barely one fingerprint image. In this letter, we introduce a novel spectral-domain OCT-based 3-D fingerprint scanner that is capable of obtaining an internal fingerprint image within 2 s. In order to obtain internal fingerprint images from raw OCT data in real time, we used graphics processing unit for massive parallel computation, along with an automated method for extracting the internal fingerprint from a 3-D scan of a fingertip. In addition, the robustness of the OCT fingerprint scanner was established by comparing fingerprint images—of wet, stained, and damaged fingertips—that were obtained by the OCT system with those from a commercially available optical fingerprint scanner.

Study on internal to surface fingerprint correlation using optical coherence tomography and internal fingerprint extraction

Surface fingerprint scanners are limited to a two-dimensional representation of the fingerprint topography, and thus, are vulnerable to fingerprint damage, distortion, and counterfeiting. Optical coherence tomography (OCT) scanners are able to image (in three dimensions) the internal structure of the fingertip skin. Techniques for obtaining the internal fingerprint from OCT scans have since been developed. This research presents an internal fingerprint extraction algorithm designed to extract high-quality internal fingerprints from touchless OCT fingertip scans. Furthermore, it serves as a correlation study between surface and internal fingerprints. Provided the scanned region contains sufficient fingerprint information, correlation to the surface topography is shown to be good (74% have true matches). The cross-correlation of internal fingerprints (96% have true matches) is substantial that internal fingerprints can constitute a fingerprint database. The internal fingerprints’ performance was also compared to the performance of cropped surface counterparts, to eliminate bias owing to information level present, showing that the internal fingerprints’ performance is superior 63.6% of the time.