Vitiligo causes localized loss of skin pigmentation, producing visible tone variations that may affect face recognition accuracy. While skin tone is a known covariate, the specific impact of vitiligo has not been studied. This work addresses two key questions: (1) does the presence of vitiligo present a challenge for face recognition systems, and (2) does the severity of vitiligo influence performance? First, we introduce Vitiligo Faces (VF), a new dataset of individuals with vitiligo, and compare matcher performance on VF against CFPW, a benchmark composed of difficult image pairs. Two of the three evaluated matchers perform worse on VF than CFPW, with greater score overlap and higher false non-match rates—indicating that vitiligo presents a unique and substantial challenge. Second, we develop a computational pipeline to quantify severity and find that increasing severity is associated with declining performance, as measured by d-prime (d’) separability. These findings underscore the need to evaluate recognition systems under underrepresented, clinically relevant conditions. Vitiligo introduces real-world variation that current models are not well equipped to handle, highlighting the importance of inclusive training and evaluation.

For the task of evaluating face recognition algorithms, the research community has adopted a set of de facto standard datasets. These datasets tend to emphasize “difficult pairs” – paired images chosen for differences in factors like age (as in AgeDB-30 and CALFW) and pose (as in CPLFW and CFP-FP). Difficult pairs allow for a more robust evaluation of algorithms, offering granular insight into the factors that are problematic for specific algorithms. However, the existing datasets ignore one factor that has historically proven highly challenging for many face recognition algorithms: race. The faces in these datasets are overwhelmingly White.In this work, we address this demographic gap with the curation of an all-Black dataset for evaluation: AgeDB-30M, where “M” indicates “melanated”. It is the first publicly-available dataset of difficult cross-age image pairs solely from the Black demographic. We hope that AgeDB-30M is a valuable tool for the research community, supporting continued efforts toward more robust algorithmic evaluation, particularly with respect to issues of bias and fairness.

Low-resolution imagery presents a critical challenge for face recognition (FR), particularly in use cases such as law enforcement and surveillance, where real-world conditions are unconstrained. Despite the development of FR systems tailored for low-resolution input and the availability of super-resolution (SR) techniques, there is no evidence that such enhancements are used in operational deployments. This work evaluates the effectiveness of six SR methods in enhancing low-resolution face images prior to recognition. We simulate low-resolution probes at interpupillary distances (IPD) of 5-30px and upscale them using SR methods, while keeping gallery images fixed at high resolution (~100px IPD). Our analysis proceeds in two stages. First, we assess whether SR methods preserve image fidelity using standard image quality assessment (IQA) metrics and 1:1 “self-matching” scores. Second, we measure their impact on biometric performance by performing 1:1 and 1:N matching. Results show that although SR techniques improve perceptual quality, they do not fully recover identity-relevant features, especially at lower resolutions. These findings highlight the limitations of current SR methods in restoring biometric utility and underscore the need for resolution-aware FR pipelines in real-world applications.

Automated one-to-many ($1: \mathrm{N}$) face recognition is a powerful investigative tool commonly used by law enforcement agencies. In this context, potential matches resulting from automated 1:N recognition are reviewed by human examiners prior to possible use as investigative leads. While automated 1:N recognition can achieve near-perfect accuracy under ideal imaging conditions, operational scenarios may necessitate the use of surveillance imagery, which is often degraded in various quality dimensions. One important quality dimension is image resolution, typically quantified by the number of pixels on the face. The common metric for this is inter-pupillary distance (IPD), which measures the number of pixels between the pupils. Low IPD is known to degrade the accuracy of automated face recognition. However, the threshold IPD for reliability in human face recognition remains undefined. This study aims to explore the boundaries of human recognition accuracy by systematically testing accuracy across a range of IPD values. We find that at low IPDs ($10 \mathrm{px}, 5 \mathrm{px}$), human accuracy is at or below chance levels ($50.7 \%, 35.9 \%$), even as confidence in decision-making remains relatively high ($77 \%, 70.7 \%$). Our findings indicate that, for low IPD images, human recognition ability could be a limiting factor to overall system accuracy.

How well can humans recognize faces at extremely low resolution? We conducted a controlled study with 100 participants to evaluate this question—and now FG2025 attendees can try it for themselves. Our interactive demo challenges attendees to match heavily degraded probe images to high-quality reference images, simulating conditions common in operational face recognition contexts. In doing so, it highlights the perceptual limits of human recognition and the risk of misidentification in high-stakes settings. The demo runs offline on standard laptops, collects no personal data, and takes about three minutes to complete.

Facial Action Units (AUs) represent individual facial muscle movements and are the building blocks for recognizing expressions and emotions. As such, AU detection is fundamental in facial affect analysis (FAA). While FAA systems are increasingly deployed in real-world applications, most AU detection models are trained and evaluated on frontfacing, well-framed images, which overlook the variability of different camera angles. To address this gap, we introduce MultiFace7, a large-scale, multi-view video corpus designed to evaluate the robustness of AU detection across camera views. Using synchronized recordings, we benchmark open-source and commercial FAA tools by extracting AU features and comparing their consistency across views using a statistical correlation analysis. Our analysis reveals substantial inconsistencies in AU intensity detection depending on the camera angle. These findings highlight limitations in current FAA systems and raise concerns about their trustworthiness in unconstrained environments where optimal camera positioning cannot be guaranteed. While a few view-invariant models and multi-view datasets exist, they are limited in scope, often rely on still images or synthetic views, and lack evidence of use in realworld FAA applications. Our work underscores the need for updated, video-based multi-view benchmarks and more robust, operationally viable AU detection models.

The last several years have seen a rapid increase in the use of containers and containerizing services, such as Docker, Kubernetes, and LXC. With such a rise comes a vast increase in opportunities for improved portability, security, and automation. While many of these opportunities are being taken advantage of in containers today, there remains many more possibilities that have not been explored. In this paper, we present a novel approach to container management that enables the rapid live migration of stateful containers between any hosts within private, public, or hybrid clouds, through a simple interface that provides a high-level view of the network of computing nodes and the containers running on them. Finally, we provide a vision for container management that allows greatly improved security and resiliency through autonomous and self-migrating containers existing across the scope of the Internet.