Recently, optical coherence tomography (OCT) has been used to noninvasively image the 3D structure of fingertip skin at high resolution. Unlike traditional 2D sensors (e.g., infrared light or capacitive technologies), the friction ridge information in 3D OCT fingerprint measurements requires reconstruction through layer segmentation. Accurate layer segmentation is helpful for fingerprint recognition and antispoofing applications. OCT volumes contain information corresponding to different directions that naturally provide complementary views. Inspired by this fact, we propose a novel orthogonal view-based attention network called OVA-Net, which exploits orthogonal views to learn the complementary information implied in the 3D fingerprint structure. Specifically, 3D convolutions and an A-line-based attention module are proposed in the B-scan view to model the long short-term intraslice correlations, whereas their counterparts in the C-scan view aim to model interslice correlations. An optical flow-based attention module is also proposed in the B-scan view to extract correlations between B-scans, which complements the interslice correlation learned in the C-scan view. Features from orthogonal views are progressively incorporated into a fusion pipeline for 3D layer segmentation. The effectiveness of OVA-Net is comprehensively evaluated in terms of layer segmentation accuracy, fingerprint reconstruction quality, and recognition performance.

Optical coherence tomography (OCT) technology enables imaging of 3D fingerprint structures. Extracting surface and internal fingerprints for identity recognition is possible by processing OCT images with layer segmentation and contour extraction. However, due to domain shift effects, OCT fingerprint extraction models often struggle to perform well across different devices. In this paper, a cross-device OCT fingerprint extraction method based on test-time image reconstruction is proposed. This method simultaneously trains layer segmentation and image reconstruction tasks during training. Additionally, a contour classification task is integrated to ensure the continuity and robustness of the contour extraction results. During the testing phase, image reconstruction is performed on test images, and the shared modules are updated to adapt the layer segmentation and contour classification network to the test domain. The result with the minimum inconsistency during the testing phase is selected as the final prediction. Experiments and comparisons are performed in terms of the distance between the ground truth and the extracted contours.

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.

Fingerprint presentation attack detection (FPAD) is essential in fingerprint identification systems. Noncontact methods such as fingerprint biometrics are becoming popular because they are not affected by skin conditions and there are no hygiene issues. However, most of the existing noncontact FPAD methods are supervised methods with poor generalizability and poor performance during events such as unseen presentation attacks (PAs). Moreover, easily overlooked frequency domain information contributes to the fingerprint antispoofing task. Therefore, we propose a wavelet-based memory-augmented autoencoder that fully utilizes the frequency domain information. Specifically, the model first decomposes the input image into high- and low-frequency information and extracts features separately. Subsequently, we propose a frequency complementary connection (FCC) module to realize the fusion and complementation of frequency domain information at the feature level. Moreover, a memory distance expansion loss is proposed to keep the memory module diverse. Experiments are conducted to verify the effectiveness of the method. The code of our model is available onhttps://github.com/SuperIOyht/WaveMemAE.

Abstract Retinal vessel segmentation is an important computer vision task for eye retinopathy diagnosis. In the real scenarios, most datasets of source domain and target domain have distribution deviation, and the model often fails to generate accurate segmentation results due to the lack of data variation in single‐source domain, which damages the generalization ability to unseen target domains and may mislead doctors or artificial intelligence model in the following diseases diagnosis. Feature normalization is one feasible solution which can standardize data into uniform and stable distribution without additional data. However, the existing methods like batch normalization, uniform the data by global parameters. This leads to insufficient representation of important semantic information in the local region. To address this problem, the authors propose the spectral‐spatial normalization (SS‐Norm) module to enhance the generalization ability of the model. More specifically, the authors perform a discrete cosine transform (DCT) to decompose the feature into multiple frequency components and to analyze the semantic contribution degree of each component. By learning a spectral vector, the authors reweight the frequency components of features and therefore normalize the distribution in the spectral domain. Extensive experiments on six datasets prove the effectiveness of the authors’ methods.

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.

Abstract Abstract Sweat pore, one of the level 3 features of fingerprint, has attracted much attention in fingerprint recognition. Traditional sweat pores on surface fingerprint are unclear or blurred when fingers are stained or damaged. Subcutaneous sweat pores, as cross section of the sweat glands, are resistant to external interferences. With 3D fingertip information measured by optical coherence tomography (OCT), the subcutaneous sweat pore estimation from OCT volume data is investigated. First, an adaptive subcutaneous pore image reconstruction method is proposed. It utilizes the skin surface and viable epidermis junction as reference and realizes depth‐adaptive pore image reconstruction. Second, a dilated U‐Net combining the U‐Net with dilated convolution is proposed for subcutaneous sweat pore extraction, which can prevent information loss of sweat pores caused by downsampling. To the best knowledge, it is the first time that subcutaneous sweat pore extraction is investigated and proposed. Experiments on subcutaneous pore image reconstruction and sweat pore extraction are both conducted. The qualitative and quantitative results show that the proposed adaptive method performs better in subcutaneous pore image reconstruction compared with the fix‐depth method, and the dilated U‐Net outperforms other methods on subcutaneous sweat pore extraction.

Abstract Retinal image analysis has become an intuitive and standard aided diagnostic technique for eye diseases. The good image quality is essential support for doctors to provide timely and accurate disease diagnosis. This paper proposes an end‐to‐end learning based method for evaluating the retinal image quality. First, blood vessels of the input image are segmented by U‐Net, and the fundus image is divided into two parts: blood vessels and background. Then, we design a dual branch network module which extracts global features that influence the image quality and suppress the interference of blood vessels and local textures to achieve better performance. The proposed module can be embedded in various advanced network structures. The experimental results show the more efficient convergence rate for the network with the module. The best network accuracy rate is 85.83%, the AUC is 0.9296, and the F1‐score is 0.7967 on the collected local dataset. Additionally, the model generalization is tested on the public DRIMDB dataset. The accuracy, AUC, and F1‐score reach 97.89%, 0.9978, and 0.9688, respectively. Compared with the state‐of‐the‐art networks, the performance of the proposed method is proven to be accurate and effective for retinal image quality assessment.

Abstract The retinal vessel is the only microvascular network that can be directly and non‐invasively observed in humans. Cardiovascular and cerebrovascular diseases, such as diabetes, hypertension, can lead to structural changes of the retinal microvascular network. Therefore, it is of great significance to study effective retinal vessel segmentation methods and assist doctors in early diagnoses with quantitative results for vascular networks. In this study, we propose a novel convolutional neural network named feature pyramid U‐Net (FPU‐Net) that extracts multiscale representations by constructing two feature pyramids both on the encoder and the decoder of U‐Net. In this representation, objects features with different size like micro‐vessels and pathology will be fused for better vessel segmentation. The experimental results show that compared with state‐of‐the‐art methods, FPU‐Net is superior in terms of accuracy, sensitivity, F1‐score, and area under the curve and capable of stronger domain generalisation across different datasets.

Abstract Early detection and treatment of skin cancer can considerably reduce the patient mortality rates. Convolutional neural network (CNN) has been widely applied in the field of computer aided diagnosis. However, for skin lesions, the inconsistent size of lesion regions in dermatoscope images hinders the convolutional neural network precise discrimination. To solve this problem, multiscale ensemble of convolutional neural networks called MECNN is proposed, which involves three branches with different lesion scales as the model input. The first branch locates the lesion region outline by identifying the largest local response point. Then, MECNN reduces the search area of the lesion region and divides the outline into two scales used as the input for the other two branches. A global loss function is defined to control the learning objectives of the three branches and MECNN fuses the branches output as the final classification result. The proposed model is evaluated on the public HAM10000 dataset and achieves a higher classification accuracy than the comparative state‐of‐the‐art methods.

Prostate cancers were segmented directly in T2-weighted images in some studies of computer-aided detection (CAD). These methods don't consider the differences between lesion and non-lesion region in T2-weighted images, so some lesions are not easy to be detected. In this paper, to consider the differences between lesion and non-lesion region, some features extraction is proposed by using graph spectral method. First, whole prostate is extracted. And then, some statistics are computed in the region of prostate to find differences between cancer and noncancer in prostate. The statistics are mapped into m dimensional Euclidean space by using graph spectral method to detect prostate cancers. Experiments show features with m dimensional Euclidean space by using graph spectral method can find some differences between lesion and non-lesion region.