Toward Synthetic Physical Fingerprint Targets.

Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms' fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels, and in the contactless fingerprint sensor setting, 3D printing and silicone filling provide the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment.

Improved automatic identification of isolated rapid eye movement sleep behavior disorder with a 3D time-of-flight camera.

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.

TeaSpam: A Novel Method of TEmporal And SPAtial Movement Encoding during Sleep.

Movements during sleep characterize sleep disorders, which can disturb sleep or its onset, impacting sleep quantity and quality. Video-polysomnography is the current gold standard to assess movements during sleep, but its availability is limited. Using data recorded with a 3D time of flight sensor, we developed a novel method of encoding temporal and spatial information of automatically identified movements during sleep. In a cohort of 20 insomnia patients and 18 controls, we showed that this novel method holds important information able to discriminate the groups. Future studies will explore the methodology in the context of other sleep disorders.