Uncertainty-aware consistency learning is one of the reliable approaches in semi-supervised medical image segmentation, enforcing robust model predictions under various perturbations. However, existing methods often rely on multiple stochastic predictions or dual-network/decoder discrepancies to estimate uncertainty, which increases computational cost and discards uncertain regions, potentially missing complex structures such as ambiguous lesion boundaries. To address these challenges, we introduce a Dual Uncertainty-Guided Consistency and Regional Contrastive Learning (DUCore) framework. DUCore improves segmentation robustness by integrating two complementary loss functions within consistency learning. The dual uncertainty-guided consistency loss (DuCL) adaptively calibrates the prediction alignment by prioritizing uncertain regions. DuCL uses deterministic single-pass uncertainty estimation, employing entropy-based calibration for aleatoric uncertainty and Proxy Dirichlet calibration for epistemic uncertainty. These uncertainty measures are computed directly from network output, and moderately uncertain regions are weighted instead of being discarded, which preserves valuable learning signals. The Regional Contrastive Loss (ReCL) further refines feature separability using boundary- and gradient-based hard negative mining in the encoded representation space. By explicitly targeting structural ambiguities, ReCL distinguishes lesion and organ edges from visually similar boundary-adjacent regions and mitigates intensity overlaps in gradient-rich transitions. As a result, DUCore is able to delineate fine structures and complex boundaries with higher precision. Extensive experiments on various medical segmentation benchmarks reveal that DUCore outperforms existing consistency methods.

Semi-supervised learning in medical image segmentation leverages unlabeled data to reduce annotation burdens through consistency learning. However, current methods struggle with class imbalance and high uncertainty from pathology variations, leading to inaccurate segmentation in 3D medical images. To address these challenges, we present DyCON, a Dynamic Uncertainty-aware Consistency and Contrastive Learning framework that enhances the generalization of consistency methods with two complementary losses: Uncertainty-aware Consistency Loss (UnCL) and Focal Entropy-aware Contrastive Loss (FeCL). UnCL enforces global consistency by dynamically weighting the contribution of each voxel to the consistency loss based on its uncertainty, preserving high-uncertainty regions instead of filtering them out. Initially, UnCL prioritizes learning from uncertain voxels with lower penalties, encouraging the model to explore challenging regions. As training progress, the penalty shift towards confident voxels to refine predictions and ensure global consistency. Meanwhile, FeCL enhances local feature discrimination in imbalanced regions by introducing dual focal mechanisms and adaptive confidence adjustments into the contrastive principle. These mechanisms jointly prioritizes hard positives and negatives while focusing on uncertain sample pairs, effectively capturing subtle lesion variations under class imbalance. Extensive evaluations on four diverse medical image segmentation datasets (ISLES’22, BraTS’19, LA, Pancreas) show DyCON’s superior performance against SOTA methods1.

This paper presents a summary of the 2025 Sclera Segmentation Benchmarking Competition (SSBC), which focused on the development of privacy-preserving sclera-segmentation models trained using synthetically generated ocular images. The goal of the competition was to evaluate how well models trained on synthetic data perform in comparison to those trained on real-world datasets. The competition featured two tracks: (i) one relying solely on synthetic data for model development, and (ii) one combining/mixing synthetic with (a limited amount of) real-world data. A total of nine research groups submitted diverse segmentation models, employing a variety of architectural designs, including transformer-based solutions, lightweight models, and segmentation networks guided by generative frameworks. Experiments were conducted across three evaluation datasets containing both synthetic and real-world images, collected under diverse conditions. Results show that models trained entirely on synthetic data can achieve competitive performance, particularly when dedicated training strategies are employed, as evidenced by the top performing models that achieved F1scores of over 0.8 in the synthetic data track. Moreover, performance gains in the mixed track were often driven more by methodological choices rather than by the inclusion of real data, highlighting the promise of synthetic data for privacy-aware biometric development. The code and data for the competition is available at: https://github.com/dariant/SSBC_2025.

Analysis of the 3-D texture is indispensable for various tasks, such as retrieval, segmentation, classification, and inspection of sculptures, knit fabrics, and biological tissues. A 3-D texture represents a locally repeated surface variation (SV) that is independent of the overall shape of the surface and can be determined using the local neighborhood and its characteristics. Existing methods mostly employ computer vision techniques that analyze a 3-D mesh globally, derive features, and then utilize them for classification or retrieval tasks. While several traditional and learning-based methods have been proposed in the literature, only a few have addressed 3-D texture analysis, and none have considered unsupervised schemes so far. This article proposes an original framework for the unsupervised segmentation of 3-D texture on the mesh manifold. The problem is approached as a binary surface segmentation task, where the mesh surface is partitioned into textured and nontextured regions without prior annotation. The proposed method comprises a mutual transformer-based system consisting of a label generator (LG) and a label cleaner (LC). Both models take geometric image representations of the surface mesh facets and label them as texture or nontexture using an iterative mutual learning scheme. Extensive experiments on three publicly available datasets with diverse texture patterns demonstrate that the proposed framework outperforms standard and state-of-the-art unsupervised techniques and performs reasonably well compared to supervised methods.

Analysis of the 3D Texture is indispensable for various tasks, such as retrieval, segmentation, classification, and inspection of sculptures, knitted fabrics, and biological tissues. A 3D texture is a locally repeated surface variation independent of the surface’s overall shape and can be determined using the local neighborhood and its characteristics. Existing techniques typically employ computer vision techniques that analyze a 3D mesh globally, derive features, and then utilize the obtained features for retrieval or classification. Several traditional and learning-based methods exist in the literature; however, only a few are on 3D texture, and nothing yet, to the best of our knowledge, on the unsupervised schemes. This paper presents an original framework for the unsupervised segmentation of the 3D texture on the mesh manifold. We approach this problem as binary surface segmentation, partitioning the mesh surface into textured and non-textured regions without prior annotation. We devise a mutual transformer-based system comprising a label generator and a cleaner. The two models take geometric image representations of the surface mesh facets and label them as texture or non-texture across an iterative mutual learning scheme. Extensive experiments on three publicly available datasets with diverse texture patterns demonstrate that the proposed framework outperforms standard and SOTA unsupervised techniques and competes reasonably with supervised methods.

Lately, there has been a surge in interest in utilizing Graph Convolutional Networks (GCNs) for the purpose of action recognition using skeletal data. In order to achieve optimal results, it is crucial to generate high-quality representations of the skeletal graph. Graph Convolutional Networks (GCNs) often employ the Message-Passing Mechanism (MPM) to acquire knowledge about various components of the skeleton by iteratively computing new features at each step. However, the interconnections between joints in the skeletal structure are intricate and extend beyond mere proximity. In order to address this issue, we propose the implementation of our Disassembled Hyper-Graph (DH-Graph), which draws inspiration from hyper-graph edges. The process of constructing the DH-network entails a few steps: partitioning the skeleton network into clusters of hyper-edges according to their semantic significance and relevance to action recognition, arranging these clusters in a hierarchical structure to enhance granularity, and establishing connections between joints within these clusters to discover hidden relationships. The DH-Graph employs a spatial domain GCN technique to construct the Pair-wise Hyper Hierarchical GCN (PH-GCN). In addition, we incorporate the HyperAttention module, which employs Multi-scale Representative Spatial Average Pooling and Edge Convolution techniques to emphasize significant sets of hyper-hierarchical information. Extensive experiments demonstrate that PH-GCN achieves remarkable performance on challenging NTU RGB+D and Northwestern UCLA datasets.

The paper provides a summary of the 2023 Unconstrained Ear Recognition Challenge (UERC), a benchmarking effort focused on ear recognition from images acquired in uncontrolled environments. The objective of the challenge was to evaluate the effectiveness of current ear recognition techniques on a challenging ear dataset while analyzing the techniques from two distinct aspects, i.e., verification performance and bias with respect to specific demographic factors, i.e., gender and ethnicity. Seven research groups participated in the challenge and submitted a seven distinct recognition approaches that ranged from descriptor-based methods and deep-learning models to ensemble techniques that relied on multiple data representations to maximize performance and minimize bias. A comprehensive investigation into the performance of the submitted models is presented, as well as an in-depth analysis of bias and associated performance differentials due to differences in gender and ethnicity. The results of the challenge suggest that a wide variety of models (e.g., transformers, convolutional neural networks, ensemble models) is capable of achieving competitive recognition results, but also that all of the models still exhibit considerable performance differentials with respect to both gender and ethnicity. To promote further development of unbiased and effective ear recognition models, the starter kit of UERC 2023 together with the baseline model, and training and test data is made available from: http://ears.fri.uni-lj.si/

Textures in 3D meshes exhibit intrinsic surface variations and are indispensable for various applications, such as retrieval, segmentation, and classification of sculptures, artifacts, and paintings. A 3D texture pattern is a locally repeated surface variation independent of the overall surface geometry and can be determined using the local neighborhood and its characteristics. Texture analysis typically employs computer vision techniques that analyze the entire 3D mesh, derive hand-crafted features, and then utilize the derived features for retrieval or classification. Several traditional and learning-based techniques exist in the literature on surface variations; however, textures are the subject of limited works. We propose a binary classification framework at the facet level for classifying texture and non-texture regions on 3D surfaces. An image sequence is generated at each facet, which serves as input to a deep vision transformer. To generate images at each facet, we construct a grid where each cell is filled with the geometric properties of its neighboring facets. We evaluated the proposed method using two datasets with diverse texture patterns, and the results are encouraging.

Abstract A fingerprint‐based authentication system provides security to the applications of numerous fields and usually stores minutiae information in the database as a template. It has been observed from the literature that the reconstruction of an original fingerprint is possible from minutiae points information; hence the security of the stored template becomes extremely crucial. Cancellable biometric techniques based on non‐invertible transformation protect the stored template. These techniques prevent the reconstruction of original fingerprint data from the compromised template and avoid unauthorized access to the system. In this paper, a technique based on the non‐invertible transformation to protect a fingerprint template is proposed. In the technique, minutiae locations in a fingerprint are transformed by using the minutiae's original locations and orientation information, and a user keyset. A principal component analysis based approach to align the probe and gallery templates of fingerprint images while matching is also proposed. The evaluation of the proposed technique is carried out on seven different fingerprint databases taken from FVC2000, FVC2002, and FVC2004 databases, and its performance is compared with other existing state‐of‐the‐art techniques in the literature. The comparative performance shows that the proposed technique is highly robust and performs exceptionally well compared to other existing techniques.

The unalterable nature of fingerprint biometrics leads to permanent identity loss of a user if an original fingerprint template is compromised. Moreover, it is evident in the literature that reconstructing a fingerprint image from an original fingerprint template is a feasible task. In order to protect the fingerprint template, we propose a noninvertible and alignment-free fingerprint template protection technique by exploiting the pair-polar minutiae structures. In the proposed technique, a secure fingerprint template is generated in the form of a set of binary vectors. These vectors are computed from the many-to-one mapping of the transformed pair-polar coordinates into a 3-D grid. Results of the proposed technique are analyzed in terms of revocability, unlinkability, security, and performance on six publicly available databases of Fingerprint Verification Competition 2002 and 2004. The overall analysis of the results clearly shows the effectiveness of the proposed technique as compared to the existing state-of-the-art techniques.