Search Results (601)

The widespread use of wearable devices has enabled continuous monitoring of biometric data, including heart rate variability (HRV) and resting heart rate (RHR). However, the validity of these measurements, particularly from consumer devices like Apple Watch, remains underexplored. This study aimed to validate HRV measurements obtained from Apple Watch Series 9 and Ultra 2 against the Polar H10 chest strap paired with the Kubios HRV software, which together served as the reference standard. A prospective cohort of 39 healthy adults provided 316 HRV measurements over a 14-day period. Generalized Estimating Equations were used to assess the difference in HRV between devices, accounting for repeated measures. Apple Watch tended to underestimate HRV by an average of 8.31 ms compared to the Polar H10 (p = 0.025), with a mean absolute percentage error (MAPE) of 28.88% and a mean absolute error (MAE) of 20.46 ms. The study found no significant impact of RHR discrepancies on HRV differences (p = 0.156), with RHR showing a mean difference of −0.08 bpm, an MAPE of 5.91%, and an MAE of 3.73 bpm. Equivalence testing indicated that the HRV measurements from Apple Watch did not fall within the pre-specified equivalence margin of ±10 ms. Despite accurate RHR measurements, these findings underscore the need for improved HRV algorithms in consumer wearables and caution in interpreting HRV data for clinical or performance monitoring. Full article

Mobile devices such as smartphones, tablets or similar devices are becoming increasingly important as measurement devices in forestry due to their advanced sensors, including RGB cameras and LiDAR systems. This review examines the current state of applications of mobile devices for measuring biometric characteristics of individual trees and presents technologies, applications, measurement accuracy and implementation barriers. Passive sensors, such as RGB cameras have proven their potential for 3D reconstruction and analysing point clouds that improve single tree-level information collection. Active sensors with LiDAR-equipped smartphones provide precise quantitative measurements but are limited by specific hardware requirements. The combination of passive and active sensing techniques has shown significant potential for comprehensive data collection. The methods of data collection, both physical and digital, significantly affect the accuracy and reproducibility of measurements. Applications such as ForestScanner and TRESTIMA^TM have automated the measurement of tree characteristics and simplified data collection. However, environmental conditions and sensor limitations pose a challenge. There are also computational obstacles, as many methods require significant post-processing. The review highlights the advances in mobile device-based forestry applications and emphasizes the need for standardized protocols and cross-device benchmarking. Future research should focus on developing robust algorithms and cost-effective solutions to improve measurement accuracy and accessibility. While mobile devices offer significant potential for forest surveying, overcoming the above-mentioned challenges is critical to optimizing their application in forest management and protection. Full article

Finger vein (FV) biometrics have garnered considerable attention due to their inherent non-contact nature and high security, exhibiting tremendous potential in identity authentication and beyond. Nevertheless, challenges pertaining to the scarcity of training data and inconsistent image quality continue to impede the effectiveness of finger vein recognition (FVR) systems. To tackle these challenges, we introduce the visual feature-guided diamond convolutional network (dubbed ‘VF-DCN’), a uniquely configured multi-scale and multi-orientation convolutional neural network. The VF-DCN showcases three pivotal innovations: Firstly, it meticulously tunes the convolutional kernels through multi-scale Log-Gabor filters. Secondly, it implements a distinctive diamond-shaped convolutional kernel architecture inspired by human visual perception. This design intelligently allocates more orientational filters to medium scales, which inherently carry richer information. In contrast, at extreme scales, the use of orientational filters is minimized to simulate the natural blurring of objects at extreme focal lengths. Thirdly, the network boasts a deliberate three-layer configuration and fully unsupervised training process, prioritizing simplicity and optimal performance. Extensive experiments are conducted on four FV databases, including MMCBNU_6000, FV_USM, HKPU, and ZSC_FV. The experimental results reveal that VF-DCN achieves remarkable improvement with equal error rates (EERs) of $0.17\%$, $0.19\%$, $2.11\%$, and $0.65\%$, respectively, and Accuracy Rates (ACC) of $100\%$, $99.97\%$, $98.92\%$, and $99.36\%$, respectively. These results indicate that, compared with some existing FVR approaches, the proposed VF-DCN not only achieves notable recognition accuracy but also shows fewer number of parameters and lower model complexity. Moreover, VF-DCN exhibits superior robustness across diverse FV databases. Full article

Count regression models, particularly negative binomial regression (NBR), are widely used in various fields, including biometrics, ecology, and insurance. Over-dispersion is likely when dealing with count data, and NBR has gained attention as an effective tool to address this challenge. However, multicollinearity among covariates and the presence of outliers can lead to inflated confidence intervals and inaccurate predictions in the model. This study proposes a comprehensive approach integrating robust and regularization techniques to handle the simultaneous impact of multicollinearity and outliers in the negative binomial regression model (NBRM). We investigate the estimators’ performance through extensive simulation studies and provide analytical comparisons. The simulation results and the theoretical comparisons demonstrate the superiority of the proposed robust hybrid KL estimator (M-NBKLE) with predictive accuracy and stability when multicollinearity and outliers exist. We illustrate the application of our methodology by analyzing a forestry dataset. Our findings complement and reinforce the simulation and theoretical results. Full article

In the rapidly evolving field of biometric authentication, deep learning has become a cornerstone technology for face detection and recognition tasks. However, traditional optimizers often struggle with challenges such as overfitting, slow convergence, and limited generalization across diverse datasets. To address these issues, this paper introduces NestYogi, a novel hybrid optimization algorithm that integrates the adaptive learning capabilities of the Yogi optimizer, anticipatory updates of Nesterov momentum, and the generalization power of stochastic weight averaging (SWA). This combination significantly improves both the convergence rate and overall accuracy of deep neural networks, even when trained from scratch. Extensive data augmentation techniques, including noise and blur, were employed to ensure the robustness of the model across diverse conditions. NestYogi was rigorously evaluated on two benchmark datasets Labeled Faces in the Wild (LFW) and YouTube Faces (YTF), demonstrating superior performance with a detection accuracy reaching 98% and a recognition accuracy up to 98.6%, outperforming existing optimization strategies. These results emphasize NestYogi’s potential to overcome critical challenges in face detection and recognition, offering a robust solution for achieving state-of-the-art performance in real-world applications. Full article

Eye-tracking is a biometrics technique that has started to find applications in research related to our interaction with the built environment. Depending on the focus of a given study, the collection of valence and arousal measurements can also be conducted to acquire emotional, cognitive, and behavioral insights and correlate them with eye-tracking data. These measurements can give architects and designers a basis for data-driven decision-making throughout the design process. In instances involving existing structures, biometric data can also be utilized for post-occupancy analysis. This entry will discuss eye-tracking and eye-tracking simulation in the context of our current understanding of the importance of our interaction with the built environment for both physical and mental well-being. Full article

Deep learning models have demonstrated significant advantages over traditional algorithms in image processing tasks like object detection. However, a large amount of data are needed to train such deep networks, which limits their application to tasks such as biometric recognition that require more training samples for each class (i.e., each individual). Researchers developing such complex systems rely on real biometric data, which raises privacy concerns and is restricted by the availability of extensive, varied datasets. This paper proposes a generative adversarial network (GAN)-based solution to produce training data (palm images) for improved biometric (palmprint-based) recognition systems. We investigate the performance of the most recent StyleGAN models in generating a thorough contactless palm image dataset for application in biometric research. Training on publicly available H-PolyU and IIDT palmprint databases, a total of 4839 images were generated using StyleGAN models. SIFT (Scale-Invariant Feature Transform) was used to find uniqueness and features at different sizes and angles, which showed a similarity score of 16.12% with the most recent StyleGAN3-based model. For the regions of interest (ROIs) in both the palm and finger, the average similarity scores were 17.85%. We present the Frechet Inception Distance (FID) of the proposed model, which achieved a 16.1 score, demonstrating significant performance. These results demonstrated StyleGAN as effective in producing unique synthetic biometric images. Full article

Electrocardiogram (ECG) signals are a measure minute electrical signals generated during the cardiac cycle, a biometric signal that occurs during vital human activity. ECG signals are susceptible to various types of noise depending on the data acquisition conditions, with factors such as sensor placement and the physiological and mental states of the subject contributing to the diverse shapes of these signals. When the data are acquired in a single session, the environmental variables are relatively similar, resulting in similar ECG signals; however, in subsequent sessions, even for the same person, changes in the environmental variables can alter the signal shape. This phenomenon poses challenges for person identification using ECG signals acquired on different days. To improve the performance of individual identification, even when ECG data is acquired on different days, this paper proposes an ensemble deep neural network for person identification by comparing and analyzing the ECG recognition performance under various conditions. The proposed ensemble deep neural network comprises three streams that incorporate two well-known pretrained models. Each network receives the time-frequency representation of ECG signals as input, and a stream reuses the same network structure under different learning conditions with or without data augmentation. The proposed ensemble deep neural network was validated on the Physikalisch-Technische Bundesanstalt dataset, and the results confirmed a 3.39% improvement in accuracy compared to existing methods. Full article

Global projections indicate that the demand for fresh water, energy, and food will increase significantly in the coming decades under the pressure of population growth, economic development, climate change, and other factors. Faced with this, technologies that promote sustainable development through the use of clean energy will be imperative. That way, this study aimed at evaluating the productive performance of biomass sorghum and cowpea cultivars in different cropping systems and planting seasons. The experiment was conducted at the Caatinga Experimental Field at Embrapa Semiarid, Petrolina—PE. Four cowpea (BRS Itaim, BRS Gurguéia, BRS Guariba, and BRS Carijó) and two biomass sorghum cultivars (BRS 716 and AGRI-002E) were used in intercropping and monoculture systems. The cultivars were sown during two different seasons: June (season 1—winter) and December (season 2—summer) of 2021. The biometric and productive parameters and land equivalent ratios (LERs) of sorghum and cowpea were evaluated. The data were subjected to multivariate analysis. The productive performance of biomass sorghum cultivars Agri-002E and BRS 716 was higher when planted in December, with an increase of 37% due to the planting season. Cowpea productivity was not influenced by sowing seasons or the cultivation system. Based on the calculation of efficient land use, the intercropping between biomass sorghum cultivar BRS 716 and cowpea cultivars BRS Gurguéia, BRS Guariba, and BRS Carijó was advantageous when compared to monocultures planted in the hottest season. This study showed the importance of cultivar selection, the planting time, and land use efficiency in intercropping systems. Full article

Background/Objectives: Very preterm infants are at increased risk of brain injury and impaired brain development. The Total Abnormality Score and biometric parameters, such as biparietal width, interhemispheric distance and transcerebellar diameter, are simple measures to evaluate brain injury, development and growth using cerebral magnetic resonance imaging data at term-equivalent age. The aim of this study was to evaluate the association between the Total Abnormality Score and biometric parameters with general movements in very preterm infants with brain injury. Methods: This single-center retrospective cohort study included 70 very preterm infants (≤32 weeks’ gestation and/or <1500 g birth weight) born between January 2017 and June 2021 in a level-three neonatal intensive care unit with brain injury—identified using cerebral magnetic resonance imaging data at term-equivalent age. General movements analysis was carried out at corrected age of 8–16 weeks. Binary logistic regression and Spearman correlation were used to examine the associations between the Total Abnormality Score and biometric parameters with general movements. Results: There was a significant association between the Total Abnormality Score and the absence of fidgety movements [OR: 1.19, 95% CI = 1.38–1.03] as well as a significant association between the transcerebellar diameter and fidgety movements (Spearman ρ = −0.269, p < 0.05). Conclusions: Among very preterm infants with brain injury, the Total Abnormality Score can be used to predict the absence of fidgety movements and may be an easily accessible tool for identifying high-risk very preterm infants and planning early interventions accordingly. Full article

Background: This study aims to compare the refractive outcomes of cataract surgery using two different biometry devices, the IOL Master 500 and IOL Master 700, and to investigate the influence of patient-related factors on these outcomes. Methods: In this retrospective study, we analyzed data from 2994 eyes that underwent cataract surgery. Multiple linear regression analyses were performed to examine the impact of the biometry device (IOL Master 500 or IOL Master 700), patient age, time elapsed between biometry and surgery, gender, and insurance status, as well as biometric parameters (anterior chamber depth, axial length, and corneal curvature), on postoperative refractive outcomes, specifically the deviation from target refraction. Results: The choice of the IOL Master device did not result in a statistically significant difference between the two devices (p = 0.205). Age (p = 0.006) and gender (p = 0.001) were identified as significant predictors of refractive outcomes, with older patients and males experiencing slightly more hyperopic outcomes compared to younger patients and females, respectively. The time elapsed between biometry and surgery and insurance status did not significantly influence the refractive outcomes. Conclusions: Our study, supported by a large cohort and a diverse group of patients representing typical anatomical variants seen in cataract surgery, supports the thesis that the IOL Master 500 and IOL Master 700 can be regarded as equivalent and effective for biometry in cataract surgery. The differences between the devices were negligible. Therefore, switching between the devices is safe for bilateral patients. Full article

Background: Intraoral scans (IOS) provide precise 3D data of dental crowns and gingival structures. This paper explores an application of IOS in human identification. Methods: We propose a dental biometrics framework for human identification using 3D dental point clouds based on machine learning-related algorithms, encompassing three stages: data preprocessing, feature extraction, and registration-based identification. In the data preprocessing stage, we use the curvature principle to extract distinguishable tooth crown contours from the original point clouds as the holistic feature identification samples. Based on these samples, we construct four types of local feature identification samples to evaluate identification performance with severe teeth loss. In the feature extraction stage, we conduct voxel downsampling, then extract the geometric and structural features of the point cloud. In the registration-based identification stage, we construct a coarse-to-fine registration scheme in order to realize the identification task. Results: Experimental results on a dataset of 160 individuals demonstrate that our method achieves a Rank-1 recognition rate of 100% using complete tooth crown contours samples. Utilizing the remaining four types of local feature samples yields a Rank-1 recognition rate exceeding 96.05%. Conclusions: The proposed framework proves effective for human identification, maintaining high identification performance even in extreme cases of partial tooth loss. Full article

Keystroke dynamics is the field of computer science that exploits data derived from the way users type. It has been used in authentication systems, in the identification of user characteristics for forensic or commercial purposes, and to identify the physical and mental state of users for purposes that serve human–computer interaction. Studies of keystroke dynamics have used datasets created from volunteers recording fixed-text typing or free-text typing. Unfortunately, there are not enough keystroke dynamics datasets available on the Internet, especially from the free-text category, because they contain sensitive and personal information from the volunteers. In this work, a free-text dataset is presented, which consists of 533 logfiles, each of which contains data from 3500 keystrokes, coming from 164 volunteers. Specifically, the software developed to record user typing is described, the demographics of the volunteers who participated are given, the structure of the dataset is analyzed, and the experiments performed on the dataset justify its utility. Full article

Objectives: This study aims to provide a comprehensive analysis of ocular biometric parameters in pediatric patients with cataracts to optimize surgical outcomes. By evaluating various biometric data, we seek to enhance the decision-making process for intraocular lens (IOL) placement, particularly with advanced technologies like femtosecond lasers. Methods: This retrospective comparative study included pediatric patients with cataracts who underwent ocular biometric measurements and cataract extraction with anterior vitrectomy at the Medical University of Vienna between January 2019 and December 2021. Parameters measured included corneal diameter (CD), axial length (AL), corneal thickness (CT) and flat and steep keratometry (Kf and Ks). The study explored the correlations between these parameters and IOL placement. Results: A total of 136 eyes from 68 pediatric patients were included in the study. Significant positive correlations were found between corneal diameter, age and AL. The mean CD was 11.4 mm, mean AL was 19.5 mm, CT was 581.2 ± 51.8 µm, Kf was 7.76 ± 0.55 mm and Ks 7.41 ± 0.59 mm, respectively. Older pediatric patients with larger corneal diameters and longer ALs were more likely to receive in-the-bag IOL implantation. Conversely, younger patients often required alternative IOL placements or remained aphakic. Our data indicated that over 95% of the study population and all patients aged one year and older had a corneal diameter of 10 mm or larger. Conclusions: Detailed ocular biometric analysis is crucial for optimizing both surgical outcomes and postoperative care in pediatric cataract patients. The positive correlations between CD, age and AL underline the importance of individualized surgical planning tailored to each patient’s unique anatomical features. Additionally, our findings suggest that the use of a femtosecond laser is both feasible and safe for pediatric patients aged one year and older, potentially offering enhanced surgical precision and improved outcomes. Full article

This study provides an in-depth analysis of GAF-Net, a novel model for video-based person re-identification (Re-ID) that matches individuals across different video sequences. GAF-Net combines appearance-based features with gait-based features derived from skeletal data, offering a new approach that diverges from traditional silhouette-based methods. We thoroughly examine each module of GAF-Net and explore various fusion methods at the both score and feature levels, extending beyond initial simple concatenation. Comprehensive evaluations on the iLIDS-VID and MARS datasets demonstrate GAF-Net’s effectiveness across scenarios. GAF-Net achieves state-of-the-art $93.2\%$ rank-1 accuracy on iLIDS-VID’s long sequences, while MARS results ($86.09\%$ mAP, $89.78\%$ rank-1) reveal challenges with shorter, variable sequences in complex real-world settings. We demonstrate that integrating skeleton-based gait features consistently improves Re-ID performance, particularly with long, more informative sequences. This research provides crucial insights into multi-modal feature integration in Re-ID tasks, laying a foundation for the advancement of multi-modal biometric systems for diverse computer vision applications. Full article