With the rapid proliferation of IoT devices, the volume of generated data is growing at an unprecedented pace. Due to the limited resources of edge devices, a significant portion of this data must be transmitted to the cloud for in-depth processing, large-scale analysis, long-term storage, and archival purposes. Consequently, the performance has become a critical concern. While identifying prevailing challenges and research gaps in this domain requires a systematic review, such efforts remain largely absent from existing survey literature. This article addresses this gap by offering a structured review of recent optimization approaches. It begins by categorizing the literature into three main strategies: lossless transmission, lossy transmission, and hybrid approaches. In the context of lossless transmission, we analyze techniques such as data compression algorithms and incremental versus full synchronization mechanisms. For lossy strategies, we analyze approaches including lossy compression and predictive methods. In addition, we investigate hybrid strategies that integrate both lossless and lossy techniques to leverage their complementary advantages. Finally, we discuss the limitations of existing studies and highlight promising directions for future research in optimizing edge-to-cloud data transmission.

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.

Small-scale object detection remains a major challenge in semi-supervised object detection (SSOD), particularly in medical image analysis. Conventional teacher models often struggle to accurately capture the features of low-contrast small lesions, leading to noisy pseudo-labels in both localization and classification, which introduces severe uncertainty and degrades detection performance. To address this issue, we propose Dual Teacher, a novel multimodal semi-supervised detection framework designed to enhance pseudo-label reliability and improve small-scale lesion detection. Specifically, we introduce two complementary teacher models: Hybrid-Scale Teacher, which exploits downsampled views to strengthen multi-scale feature learning, and Entropy-Based Multi-Modal Teacher, which leverages entropy maps to refine the quality of small-scale pseudo-labels. To effectively fuse predictions from both teachers and resolve conflicts, we propose a Dempster-Shafer-based Dual-Teacher pseudo-label fusion strategy that explicitly models uncertainty and optimizes classification confidence. Additionally, we introduce a class-adaptive threshold mechanism that dynamically adjusts pseudo-label selection based on dual-teacher predictions, further boosting the recall of small-scale lesions. Extensive experiments on the Dental Disease Dataset, ChestX-Det and M3FD demonstrate that our method consistently surpasses state-of-the-art SSOD approaches. Code is available at: https://github.com/z316910/Dual-Teacher.git.

Underwater target detection is primarily achieved through two methods: optical imaging and underwater sonar. 3D sonar, as the most advanced underwater detection technology, is characterized by strong penetration and long scanning distance, making it more suitable for tasks such as deep-sea exploration, murky water detection, and long-distance target identification. However, acquiring underwater sonar images is challenging, and there is no open-source 3D sonar dataset. Traditional three-dimensional target detection methods typically require highquality data and face significant challenges when dealing with weak heterogeneous sonar point clouds caused by high noise, low resolution, and occlusions. To address the aforementioned issues, we first propose a novel fuzzy decoupling module that differs from traditional foreground-background segmentation. This module simultaneously extracts valuable information about the target and its surrounding environment, mitigating the reduction in heterogeneity caused by noise and sonar side lobes. To achieve efficient fusion and capture global information after fuzzy decoupling, a multi-hop Mamba seamless adaptive decoupling point is introduced. It effectively enhances the connection between the two decoupled parts. To address missing and occlusion problems, a second-stage refinement based on Markov prediction is proposed. This low-cost approach, in contrast to using the original point cloud for contour and detail completion, enriches target boundary information. To validate our method, we have designed a practical 3D sonar imaging system and tested it through lake-based experiments. We have collected extensive raw data from Qiandao Lake and conducted annotation work. Through qualitative and quantitative experiments, our method outperforms the most advanced methods by 11.4%.

Optical Coherence Tomography (OCT) is a high-resolution, non-invasive imaging technology increasingly used for biometric data collection from fingertips. OCT captures volume data up to 3mm below the skin surface in the form of a series of B-scan images, enabling the reconstruction of internal fingerprints (IF) and internal sweat pores (ISP), thereby enhancing the security of biometric recognition. Despite the advantages, OCT images suffer from speckle noise and tissue discontinuity, making the extraction of subcutaneous biometric features challenging. Traditional hardware and software-based enhancement methods often result in over-smoothing and structural loss. Recent advancements in deep learning (DL) offer promising alternatives, with supervised DL methods showing efficacy when trained with high-quality paired datasets. However, the absence of ground-truth (GT) data makes it impossible to apply these models. This study proposes a novel supervised enhancement method for fingertip OCT images, with a paired dataset generation strategy. An OCT few-shot GAN and a Quality Estimation Module are proposed and incorporated into the strategy to realize translation from minimal GT manual augmentation to high-quality paired dataset, effectively addressing the challenge of data scarcity. A Fast Supervised Enhancement GAN (FSE-GAN) is proposed thereafter to perform simultaneous speckle noise reduction and tissue structure restoration, facilitating accurate extraction of internal fingerprints and sweat pores. Experiments demonstrate that the enhanced images significantly simplify IF and ISP extraction while achieving outstanding result quality.

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.

Advancements in internal fingerprint extraction technology have made the fusion of external and internal fingerprints possible. It offers a viable solution to the problem of degraded performance in Automatic Fingerprint Identification System (AFIS) caused by epidermal abrasion and aging. Traditional fusion methods focus on information maximization. But for fingerprint, features like wrinkles and scars often yield high gradient variation information. It is detrimental to generating high-quality fingerprint. To address this, we propose a novel quality driven fusion method for external and internal fingerprints. It comprises several components. Firstly, there is a lightweight and efficient Transformer-CNN hybrid model. Secondly, it includes a closed-loop quality driven fusion mechanism. This mechanism is equipped with a quality prediction module, Weighted Complementary Fusion (WCF), and quality feedback. Thirdly, there is a jointly optimized combined loss function, which is accompanied by an asynchronous cross-training strategy. Unlike traditional paradigms, we change the optimization objective. It is shifted from information maximization to quality maximization, which is more appropriate for fingerprint. Experimental evaluations have been conducted, covering aspects such as fingerprint quality, matching performance, and network model ablation. The method we proposed demonstrates superiority in terms of quality score and matching performance. It outperforms both traditional and state-of-the-art approaches. It gives a new research path to boost fingerprint identification performance in identity security authentication.

In the domain of language-based fashion image retrieval, pinpointing the desired fashion item using both a reference image and its accompanying textual description is an intriguing challenge. Existing approaches lean heavily on static fusion techniques, intertwining image and text. Despite their commendable advancements, these approaches are still limited by a deficiency in flexibility. In response, we propose a Self-distilled Dynamic Fusion Network to compose the multi-granularity features dynamically by considering the consistency of routing path and modality-specific information simultaneously. Two new modules are included in our proposed method: (1) Dynamic Fusion Network with Modality Specific Routers. The dynamic network enables a flexible determination of the routing for each reference image and modification text, taking into account their distinct semantics and distributions. (2) Self Path Distillation Loss. A stable path decision for queries benefits the optimization of feature extraction as well as routing, and we approach this by progressively refine the path decision with previous path information. Extensive experiments demonstrate the effectiveness of our proposed model compared to existing methods.

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.

Chinese characters are complex and contain discriminative information, meaning that their writers have the potential to be recognized using less text. In this study, offline Chinese writer identification based on a single character was investigated. To extract comprehensive features to model Chinese characters, explicit and implicit information as well as global and local features are of interest. A dual-branch multitask fusion network is proposed that contains two branches for global and local feature extraction simultaneously, and introduces auxiliary tasks to help the main task. Content recognition, stroke number estimation, and stroke recognition are considered as three auxiliary tasks for explicit information. The main task extracts implicit information of writer identity. The experimental results validated the positive influences of auxiliary tasks on the writer identification task, with the stroke number estimation task being most helpful. In-depth research was conducted to investigate the influencing factors in Chinese writer identification, with respect to character complexity, stroke importance, and character number, which provides a systematic reference for the actual application of neural networks in Chinese writer identification.

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.

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.

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.