Background and purpose: Automatic 3D video analysis of the lower body during rapid eye movement (REM) sleep has been recently proposed as a novel tool for identifying people with isolated REM sleep behavior disorder (iRBD), but, so far, it has not been validated on unseen subjects. This study aims at validating this technology in a large cohort and at improving its performances by also including an analysis of movements in the head, hands and upper body.Methods: Fifty‐three people with iRBD and 128 people without RBD (of whom 89 had sleep disorders considered RBD differential diagnoses) were included in the study. An automatic algorithm identified movements from 3D videos during REM sleep in four regions of interest (ROIs): head, hands, upper body and lower body. The movements were divided into categories according to duration: short (0.1–2 s), medium (2–15 s) and long (15–300 s). For each ROI and duration range, features were obtained from the identified movements. Logistic regression models using as predictors the features from one single ROI or a combination of ROIs were trained and tested in a 10‐runs 10‐fold cross‐validation scheme on the task of differentiating people with iRBD from people without RBD.Results: The best differentiation was achieved using short movements in all four ROIs (test accuracy 0.866 ± 0.007, test F1 score = 0.783 ± 0.010). Single group analyses showed that people with iRBD were distinguished successfully from subjects with RBD differential diagnoses.Conclusions: Automatic 3D video analysis might be implemented in clinical routine as a supportive screening tool for identifying people with RBD.

We propose MCLFIQ: Mobile Contactless Fingerprint Image Quality, the first quality assessment algorithm for mobile contactless fingerprint samples. To this end, we re-trained the NIST Fingerprint Image Quality (NFIQ) 2 method, which was originally designed for contact-based fingerprints, with a synthetic contactless fingerprint database. We evaluate the predictive performance of the resulting MCLFIQ model in terms of Error-vs.-Discard Characteristic (EDC) curves on three real-world contactless fingerprint databases using three recognition algorithms. In experiments, the MCLFIQ method is compared against the original NFIQ 2 method, a sharpness-based quality assessment algorithm developed for contactless fingerprint images and the general purpose image quality assessment method BRISQUE. Furthermore, benchmarks on four contact-based fingerprint datasets are also conducted. Obtained results show that the fine-tuning of NFIQ 2 on synthetic contactless fingerprints is a viable alternative to training on real databases. Moreover, the evaluation shows that our MCLFIQ method works more accurately and is more robust compared to all baseline methods on contactless fingerprints. We suggest considering the proposed MCLFIQ method as a starting point for the development of a new standard algorithm for contactless fingerprint quality assessment.

Current fingerprint identification systems face significant challenges in achieving interoperability between contact‐based and contactless fingerprint sensors. In contrast to existing literature, we propose a novel approach that can combine pose correction with further enhancement operations. It uses deep learning models to steer the correction of the viewing angle, therefore enhancing the matching features of contactless fingerprints. The proposed approach was tested on real data of 78 participants (37,162 contactless fingerprints) acquired by national police officers using both contact‐based and contactless sensors. The study found that the effectiveness of pose correction and unwarping varied significantly based on the individual characteristics of each fingerprint. However, when the various extension methods were combined on a finger‐wise basis, an average decrease of 36.9% in equal error rates (EERs) was observed. Additionally, the combined impact of pose correction and bidirectional unwarping led to an average increase of 3.72% in NFIQ 2 scores across all fingers, coupled with a 6.4% decrease in EERs relative to the baseline. The addition of deep learning techniques presents a promising approach for achieving high‐quality fingerprint acquisition using contactless sensors, enhancing recognition accuracy in various domains.

We study photoexcitations in small Hubbard clusters of up to 12 sites. After the electric field pulse some of these clusters show an increase of the double occupation through impact ionization. We treat the time-dependent electromagnetic field classically and calculate time evolution by exact diagonalization. As a tool for better analyzing the out-of-equilibrium dynamics, we generalize the Loschmidt amplitude. In this way we are able to resolve which many-body energy eigenstates are responsible for impact ionization and which ones show pronounced changes in the double occupation and spin energy. Our analysis reveals that the increase of spin energy is of little importance for impact ionization. We further demonstrate that, for one-dimensional chains, the optical conductivity has a characteristic peak structure originating solely from vertex corrections.

Contactless fingerprint recognition systems offer a hygienic, user-friendly, and efficient alternative to traditional contact-based methods. However, their accuracy heavily relies on precise fingertip detection and segmentation, particularly under challenging background conditions. This paper introduces TipSegNet, a novel deep learning model that achieves state-of-the-art performance in segmenting fingertips directly from grayscale hand images. TipSegNet leverages a ResNeXt-101 backbone for robust feature extraction, combined with a Feature Pyramid Network (FPN) for multi-scale representation, enabling accurate segmentation across varying finger poses and image qualities. Furthermore, we employ an extensive data augmentation strategy to enhance the model’s generalizability and robustness. This model was trained and evaluated using a combined dataset of 2257 labeled hand images. TipSegNet outperforms existing methods, achieving a mean intersection over union (mIoU) of 0.987 and an accuracy of 0.999, representing a significant advancement in contactless fingerprint segmentation. This enhanced accuracy has the potential to substantially improve the reliability and effectiveness of contactless biometric systems in real-world applications.

While biometric data, such as fingerprints, are increasingly used for identification and authentication, their inability to be revoked once compromised raises privacy concerns. To address these concerns, this work explores the use of Multiparty Computation (MPC), which enables secure computations on encrypted data for privacy-preserving fingerprint matching. Despite MPC's well-known drawback of slowing down computation, recent advances have made it a viable option for real-world applications. Our research focuses on implementing and optimizing a minutiae-based fingerprint matching algorithm with MPC that addresses the challenge of preserving privacy while ensuring reasonable computation times. Furthermore, this work presents a privacy-preserving implementation of SourceAFIS using the MP-SPDZ framework. In addition, we demonstrate the adaptability of the MPC implementation by integrating different minutiae extractors, leading to significant enhancements in error rates. These findings underscore the feasibility and effectiveness of using modern MPC techniques for privacy-preserving fingerprint matching and paves the way for further optimizations.