In the contemporary technological landscape, biometrics, encompassing the analysis of biological data, have gained significance. Biometrics is a methodology that utilize unique behavioral, physical, or morphological traits—such as speech, facial features, iris, fingerprint, retina, and signature for individual identification. Biometric technology has been successfully used in forensic science, security, and authorization systems. This review highlights understanding the classification, types of biometric traits and their comparisons, fingerprint recognition stands out as a reliable and widely adopted method due to its simplicity and cost-effectiveness, accuracy and robustness compared to others. Unimodal fingerprint biometric systems safeguard authentication information through the analysis of characteristic sequences and, face challenges such as vulnerability to spoof attacks, inter-class similarity, intra-class variation, non-universality, and noisy data. These challenges are addressed by multimodal fingerprint biometric systems, in which various biometric sources compensate for each other’s limitations. The review focuses on the overview of unimodal and multimodal fingerprint biometric systems and the importance of fusion, advancements in data acquisition, preprocessing, feature extraction, matching algorithms, performance metrics, indexing, template protection, and addressing attacks in enhancing system security and reliability. The review paper sheds light on the intricate relationship between these elements, offering valuable insights into the current state and potential evolution of the field. The review paper highlights current challenges and suggests future research directions, emphasizing the necessity for continual advancements in fusion techniques, template protection methods, and novel defense mechanisms to effectively mitigate emerging threats in unimodal and multimodal fingerprint biometric systems.

Effective cardiovascular health monitoring relies on precise electrocardiogram (ECG) analysis for early diagnosis and treatment of heart conditions. Recent advancements in deep learning, particularly through Convolutional Neural Networks (CNNs), have significantly enhanced the automation, accuracy, and personalization of ECG analysis. This review targets both medical professionals and a broader audience interested in deep learning applications. It explores the evolution of deep learning techniques in ECG analysis, from early CNN applications to current innovations in real-time processing and privacy-preserving methods. The paper discusses various deep learning models, including hybrid models, Recurrent Neural Networks (RNNs), and attention mechanisms, and their impact on diagnostic accuracy for diseases like myocardial infarction. Additionally, it examines ECG-based authentication systems, addressing challenges related to security and privacy, and highlighting recent technological advancements. By providing a detailed overview of these developments, the review offers valuable insights into future directions for deep learning in cardiovascular health monitoring and ECG-based authentication.

The use of finger veins for biometric authentication is increasingly popular; however, low-quality images present significant challenges that necessitate innovative approaches for accurate identification. This study investigates the effectiveness of various image preprocessing techniques to enhance and analyze finger vein images for biometric recognition. We utilized a finger vein dataset from Kaggle, comprising diverse images captured under controlled conditions. Our preprocessing methods included sharpening images using convolution kernels to improve edge definition, employing thresholding techniques (simple binary, adaptive, and Otsu’s) for effective image segmentation, and applying morphological operations (erosion, dilation, opening, and closing) to refine object shapes and reduce noise. We also implemented edge detection methods, including Sobel, Laplacian, and Canny, to identify significant boundaries within the images. Image resizing was performed using linear, cubic, and area interpolation to assess their effects on image quality. Additionally, various filtering techniques—such as Kalman, median, and Gaussian filters—were applied to reduce noise and enhance image clarity. The dataset comprised 3,816 images from 106 individuals, split into two configurations: 80-10-10 and 70-15-15. We assessed models such as VGG16, VGG19, ResNet101, AlexNet, MobileNet, DenseNet201, and EfficientNet based on metrics including accuracy, precision, recall, F1-score, and training time. VGG16, VGG19, and ResNet101 achieved accuracies of 99.9%, 99.8%, and 99.8%, respectively. Data augmentation techniques generated 76,320 augmented images, significantly improving model performance, especially for the 80-10-10 split. Visualizations through radar and bar charts indicated that VGG16, VGG19, and ResNet101 delivered the highest performance metrics, while DenseNet201 exhibited a slight decline in the 70-15-15 split due to increased test data. Overall, the findings demonstrate the models’ efficacy for reliable finger vein biometric recognition, contributing to advancements in biometric authentication systems.

Deep learning and fully homomorphic encryption (FHE) are integrated for privacy-preserving fingerprint recognition. Convolutional neural network (CNN) extract fingerprint features encrypted using the Cheon-Kim-Kim-Song (CKKS) FHE scheme. TenSEAL ensures all computations occur in the encrypted domain, preventing raw biometric data exposure during authentication. A subset of the SOCOFing dataset, comprising 7,200 altered fingerprints from 90 individuals across three difficulty levels, is used for training (80%), validation (10%), and testing (10%). One real fingerprint per user is encrypted and stored for authentication, reducing computational complexity. The CNN classifies encrypted features without decryption, ensuring secure authentication. The system, utilizing Euclidean similarity, achieves 99.06% test accuracy with a loss of 0.1692, a True Acceptance Rate (TAR) of 99.19%, a False Rejection Rate (FRR) of 0.81%, a False Acceptance Rate (FAR) of 0%, and a True Rejection Rate (TRR) of 100%, with a minimal Equal Error Rate (EER) of 0.40%. Encrypted templates require only 31 MB for 90 users, averaging 344 KB per fingerprint per user. Despite a 157.26× encryption overhead, remains feasible for real-time applications, completing an encrypted comparison in 0.025 seconds with a total processing time of 0.136 seconds per fingerprint. Adhering to ISO/IEC 24745 biometric protection standards, the system ensures irreversibility, unlinkability, and renewability of biometric templates. Decryption occurs only on the client side, keeping raw fingerprint data inaccessible to the server. These results confirm the feasibility of FHE for secure fingerprint authentication in cloud-based environments, benefiting applications in healthcare, banking, and access control.

Background: Sexual violence is cancerous, and it malignantly affects all strata of our community regardless of age, gender, race, ability, and social status. Excellent healthcare service requires professionals who can do a sexual assault examination, collect medico-legal evidence, take photographs of patients' anatomy that was involved in the assault and document what is there and where it is in relation to the victims or perpetrators body or other parts of the crime scene, whether or not it is connected to the crime. Main text: The objective of this article to orient the professions to the novel trends and approaches in forensic photography, namely e-consent, digital cameras and their accessories, dedicated forensic software, and photographic protocols and procedures. Conclusion: The inadequate and improper collection, preservation and presentation of evidence can lead to crime scene errors and fewer conviction rates. Training in forensic photography is vital and a game-changer in the sexual assault examination.

Compared with 2D face recognition systems, 3D facial recognition has been a popular area of study in academia and industry, because of its ability to detect human faces in an unconstrained environment more efficiently. This study presents a new 3D face identification method that uses 3D facial images to compute geodesic distances using 3D landmarks. Feature extraction is computationally more efficient than Euclidean distance using geodesic distance. This study aims to demonstrate how to differentiate identical twins based solely on facial features by developing an efficient algorithm for calculating geodesic paths and distances. We employed the A*, Dijkstra, and fast marching (FM) algorithms to calculate the geodesic distance. Quantitative similarity metrics are obtained and then utilized as inputs in numerous classification methods: random forest (RF), extra tree classifier (ETC), light gradient boosting machine (LGBM), support vector machine (SVM), and bagging classifiers. An expression challenge dataset for 3D twins (3D-TEC), the most challenging data set for 3D face recognition research at Notre Dame University, was used to validate and test the performance of these algorithms. The recall, accuracy, precision, training time, and F1 score were compared for each classifier to determine the best performance. The results from the experiment indicate that, when compared to other algorithms and classification models, the Extra Tree Classifier is the most accurate (by 90%) in predicting face images, whereas A* is the best technique for computing geodesic distance.