LivDet-Iris 2025 is the sixth edition of the iris liveness detection competition. Held every two to three years, the competition aims to foster the development of robust algorithms capable of detecting a wide range of physically-and digitally-presented attacks in iris biometrics. The 2025 edition obtained the largest number of submissions in the history of the competition: ten algorithms from five institutions, and one commercial iris recognition system. LivDet-Iris 2025 also introduced new tasks compared to previous editions: (Task 1) a benchmark offered by an industry partner, (Task 2) morphed iris images, in which two different-identity samples were blended into one image, and (Task 3) evaluation of presentation attack detection robustness against advanced manufacturing techniques for textured contact lenses. This edition, for the first time in the series, offers a systematic testing of a commercial iris recognition system (software and hardware) using physical artifacts presented to the sensor. Dermalog-Iris team submitted algorithms that won all tasks, achieving the area under the ROC curve of 90.57%, 68.23% and 99.99% in tasks 1, 2, and 3, respectively. Additionally, we include results for baseline algorithms, based on modern deep convolutional neural networks and trained with all available public datasets of iris images representing bona fide samples and anomalies (physical attacks, eye diseases, post-mortem cases, and synthetically-generated iris images). Test samples created for tasks 2 and 3, and baseline models are made available to offer the state-of-the-art benchmark for iris liveness detection.

Advancements in virtual and head-mounted devices have introduced new challenges for iris biometrics, such as varying gaze directions, partial occlusions, and inconsistent lighting conditions. To address these obstacles, we present VREyeSAM, a robust iris segmentation framework specifically designed for images captured under both steady and dynamic gaze scenarios. Our pipeline includes a quality-aware pre-processing module that filters out partially or fully closed eyes, ensuring that only high-quality, fully open iris images are used for training and inference. In addition, we introduce an uncertainty weighted hybrid loss function that adaptively balances multiple learning objectives, enhancing the robustness of the model under diverse visual conditions. Using this approach, we evaluate VREyeSAM on the VRBiom dataset, where it achieves state-of-the-art performance with a Precision of 0.751, Recall of 0.870, F1-Score of 0.806, and a mean IoU of 0.647, significantly outperforming existing segmentation methods.

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.

ABSTRACT Ontology engineering is crucial for many areas such as information retrieval systems, data integration facilities, and basic decision support systems. Nevertheless, estimating the cost of ontology engineering projects is notoriously difficult to achieve. This challenge stems from the complexity and evolving nature of such projects. To solve this difficulty, we propose to improve the accuracy of cost estimation through a hybrid methodology that combines Fuzzy Ontology Cost Estimation Model (F‐ONTOCOM) and Artificial Neural Networks (ANN). Fuzzy logic is used in our model to capture linguistic variables and other complex relationships within the scope of cost estimation. At the same time, ANN allows for the recognition of complex nonlinear interactions, enhancing the overall accuracy of prediction. This integration of fuzzy logic and neural networks leads to enhancements in the model's robustness, adaptability, and precision. Our approach features a methodology for 148 ontology engineering projects that include, but are not limited to, data scraping and preprocessing, fuzzy inference system design, neural network training, and validation processes. The results showed that the hybrid approach was champion over the traditional estimation approach in terms of effort estimation, Mean Relative Error (MRE), Mean Magnitude of Relative Error (MMRE), and the predictive accuracy over 21 randomly selected ontology projects.

Abstract Resource management in computing presents significant challenges that require innovative solutions. This paper proposes a novel architecture integrating artificial intelligence (AI) with model order reduction (ROM) and advanced queueing theory models to enhance resource allocation and task scheduling efficiency. The study demonstrates substantial improvements in critical performance parameters, including response time optimization, resource utilization, and energy consumption management through comprehensive mathematical modeling and machine learning frameworks. The methodology incorporates predictive analytics for resource demand forecasting, intelligent scheduling algorithms for automatic workload adaptation, and calendar queueing techniques for real-time decision-making. Extensive simulations and analyses across multiple queueing scenarios validate the theoretical framework, establishing a robust foundation for efficient computation in large-scale distributed environments. Results indicate a 50% reduction in response time, a 50% increase in throughput, and a 15% improvement in resource utilization. The ROM implementation achieved a 65–80% reduction in processing overhead while maintaining 95–98% accuracy compared to full-scale models. Energy efficiency improved by 20% through intelligent workload distribution, with system reliability reaching 99.99% uptime. This research contributes to computing advancement by demonstrating the effectiveness of integrating AI-driven resource management with traditional queueing theory, providing a scalable solution for modern infrastructure optimization. The proposed framework’s ability to automatically adapt to varying workloads while maintaining optimal performance parameters represents a significant step forward in resource management technology.

Recent studies have emphasized the potential of forehead-crease patterns as an alternative for face, iris, and periocular recognition, presenting contactless and convenient solutions, particularly in situations where faces are covered by surgical masks. However, collecting forehead data presents challenges, including cost and time constraints, as developing and optimizing forehead verification methods requires a substantial number of high-quality images. To tackle these challenges, the generation of synthetic biometric data has gained traction due to its ability to protect privacy while enabling effective training of deep learning-based biometric verification methods. In this paper, we present a new framework to synthesize forehead-crease image data while maintaining important features, such as uniqueness and realism. The proposed framework consists of two main modules: a Subject-Specific Generation Module (SSGM), based on an image-to-image Brownian Bridge Diffusion Model (BBDM), which learns a one-to-many mapping between image pairs to generate identity-aware synthetic forehead creases corresponding to real subjects, and a Subject-Agnostic Generation Module (SAGM), which samples new synthetic identities with assistance from the SSGM. We evaluate the diversity and realism of the generated forehead-crease images primarily using the Fréchet Inception Distance (FID) and the Structural Similarity Index Measure (SSIM). In addition, we assess the utility of synthetically generated forehead-crease images using a forehead-crease verification system (FHCVS). The results indicate an improvement in the verification accuracy of the FHCVS by utilizing synthetic data.

This paper presents the summary of the Sclera Segmentation and Joint Recognition Benchmarking Competition (SSRBC 2023) held in conjunction with IEEE International Joint Conference on Biometrics (IJCB 2023). Different from the previous editions of the competition, SSRBC 2023 not only explored the performance of the latest and most advanced sclera segmentation models, but also studied the impact of segmentation quality on recognition performance. Five groups took part in SSRBC 2023 and submitted a total of six segmentation models and one recognition technique for scoring. The submitted solutions included a wide variety of conceptually diverse deep-learning models and were rigorously tested on three publicly available datasets, i.e., MASD, SBVPI and MOBIUS. Most of the segmentation models achieved encouraging segmentation and recognition performance. Most importantly, we observed that better segmentation results always translate into better verification performance.