Neonatal biometrics, especially those based on fingerprint traits, can potentially improve early childhood identification with decisive applications in healthcare, identity management, and other critical social domains. Although many biometric approaches to human recognition exist, most of them cannot be directly applied to neonates. The main barrier is the reduced size of children’s biometric traits, which affects image quality as these traits are still developing. Another issue is the lack of child biometric databases, as a periodic recollection of images is a fundamental part of neonatal identification regarding the feasibility evaluation of temporal recognition. Several works can be found in the literature addressing some of these issues. However, there is still no systematic review allowing a general understanding of these solutions, discussing their links, gaps, comparisons, and open challenges. In this sense, this article presents a systematic literature review on neonatal biometrics. In total, 1,878 papers were screened and classified, resulting in 45 being selected to be analyzed in this study. We detail and compare the results of datasets, scanners, methods, and techniques to achieve and improve neonatal recognition. Finally, research trends are identified and discussed based on the main gaps in the literature.

This study tackles the challenge of precise wood log counting, where applications of the proposed methodology can span from automated approaches for materials management, surveillance, and safety science to wood traffic monitoring, wood volume estimation, and others. We introduce an approach leveraging Conditional Generative Adversarial Networks (cGANs) for eucalyptus log segmentation in images, incorporating specialized image processing techniques to handle noise and intersections, coupled with the Connected Components Algorithm for efficient counting. To support this research, we created and made publicly available a comprehensive database of 466 images containing approximately 13,048 eucalyptus logs, which served for both training and validation purposes. Our method demonstrated robust performance, achieving an average ${\mathrm{Accuracy}}_{pixel}$ of 96.4% and ${\mathrm{Accuracy}}_{logs}$ of 92.3%, with additional measures such as F1 scores ranging from 0.879 to 0.933 and IoU values between 0.784 and 0.875, further validating its effectiveness. The implementation proves to be efficient with an average processing time of 0.713 s per image on an NVIDIA T4 GPU, making it suitable for real-time applications. The practical implications of this method are significant for operational forestry, enabling more accurate inventory management, reducing human errors in manual counting, and optimizing resource allocation. Furthermore, the segmentation capabilities of the model provide a foundation for advanced applications such as eucalyptus stack volume estimation, contributing to a more comprehensive and refined analysis of forestry operations. The methodology’s success in handling complex scenarios, including intersecting logs and varying environmental conditions, positions it as a valuable tool for practical applications across related industrial sectors.

While fingerprint biometrics are a mature technology, preprocessing steps such as image alignment and minutiae filtering still significantly impact their performance. To address these challenges, this paper introduces a comprehensive package of analytical methods designed to enhance fingerprint image similarity analysis. Initially, we present an alignment method based on partitioning and statistical image analysis, which does not rely on specific information, such as the location of singular points. Next, a minutiae filtering method is developed, focusing on detecting and removing edge minutiae, which tend to provide non-discriminative information. To establish an agnostic distance metric approach, we conducted extensive experiments on public fingerprint datasets, incorporating an ablation study to evaluate the impact of alignment and filtering methods. Our results demonstrate that the proposed combination of rotation and filtering significantly outperforms untreated images, providing an efficient and competitive solution that improves and contributes to the biometric analysis field.

The escalating complexity of electrical energy systems demands innovative approaches to power management and predictive analysis. This groundbreaking study introduces a novel machine learning methodology for power factor prediction within a university campus setting, leveraging a sophisticated data normalization technique to address asymmetries in data collection. By systematically comparing multiple machine learning algorithms—including Logistic Regression, Random Forests, Support Vector Machines (SVM), k-Nearest Neighbors, and Multi-Layer Perceptron Neural Networks—the research provides insights into predictive performance. Notably, the research also developed an innovative data standardization mechanism using arithmetic functions, which effectively mitigates data asymmetry challenges. These results not only advance our understanding of power factor dynamics but also offer a robust framework for energy management in complex electrical systems, particularly in regions with adversarial operational characteristics.