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.

Presentation attacks on biometric systems have long created significant security risks. The increase in the adoption of age verification systems, which ensure that only age-appropriate content is consumed online, raises the question of vulnerability of such systems to replay presentation attacks. In this paper, we analyze the vulnerability of face age verification to simple replay attacks and assess whether presentation attack detection (PAD) systems created for biometrics can be effective at detecting similar attacks on age verification. We used three types of attacks captured with iPhone 12, Galaxy S9, and Huawei Mate 30 phones from iPad Pro, which replayed the images from a commonly used UTKFace dataset of faces with true age labels. We evaluated four state of the art face age verification algorithms, including simple classification, distribution-based, regression via classification, and adaptive distribution approaches. We show that these algorithms are vulnerable to the attacks, since the accuracy of age verification on replayed images is only a couple of percentage points different compared to when the original images are used, which means an age verification system cannot distinguish attacks from bona fide images. Using two state of the art presentation attack detection systems, DeepPixBiS and CDCN, trained to detect similar attacks on biometrics, we demonstrate that they struggle to detect both: the types of attacks that are possible in age verification scenario and the type of bona fide images that are commonly used. These results highlight the need for the development of age verification specific attack detection systems for age verification to become practical.

Recent developments in Virtual Reality (VR) headsets have unlocked a plethora of innovative use-cases, many of which were previously unimaginable. However, as these use-cases, such as personalized immersive experiences, necessitate user authentication, ensuring robustness and resistance to spoofing attacks becomes imperative. The absence of appropriate dataset has constrained our understanding and assessment of VR devices’ susceptibility to presentation attacks. To address this research gap, we introduce VRBiom: a new periocular video dataset acquired from a VR headset (Meta Quest Pro), comprising 900 genuine and 1104 presentation attack videos, each spanning 10 seconds. The bona-fide videos consist of variations in terms of gaze and glasses; while the attacks are constructed with 6 different types of instruments. Additionally, we evaluate the performance of two prominent CNN architectures trained using various configurations for detecting presentation attacks in the newly created VRBiom dataset. Our benchmarking on VRBiom reveals the presence of spoofing threats in VR headsets. While baseline models exhibit considerable efficacy in attack detection, substantial scope exists for improvement in detecting attacks on periocular videos. Our dataset will be a useful resource for researchers aiming to enhance the security and reliability of VR-based authentication systems.

Detection of interesting points in the image is an important phase when considering object detection problem in computer vision. Corners are good candidates as such interest points. In this study, by optimizing corner model of Ghosal based on local Zernike moments (LZM) and using LZM representation Sariyanidi et.al presented, a rotation-invariant interest point detector is proposed. The performance of proposed detector is evaluated by using Mikolajczyk's dataset prepared for rotation-invariance and our method outperforms well-known methods such as SIFT and SURF in terms of repeatability criterion.