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.

The federated learning (FL) principle ensures multiple clients jointly develop a machine learning model without exchanging their local data. Various government enacted prohibition policies on data exchange between organizations have led to the need for privacy-preserved federated learning. Many industries have cultivated this idea of model development through federated learning to enhance performance and accuracy. This paper offers a detailed overview of the background of FL, highlighting existing aggregation algorithms, frameworks, implementation aspects, and dataset repositories, establishing itself as an essential reference for researchers in the field. The paper thoroughly reviews existing centralized and decentralized FL approaches proposed in the literature and gives an overview about the methodology, privacy techniques implemented and limitations to guide other researchers to advance their research in the field of federated learning. The paper discusses the critical role of privacy-enhancing technologies like differential privacy (DP), homomorphic encryption (HE), and secure multiparty computation (SMPC) in federated learning highlighting their effectiveness in safeguarding sensitive data while optimizing the balance between privacy, communication efficiency, and computational cost. The paper explores the applications of federated learning in privacy-sensitive areas like natural language processing (NLP), healthcare, and Internet of Things (IoT) with edge computing. We believe our work provides a novel addition by identifying privacy evaluation metrics and spotlighting the measures in terms of data privacy and correctness, communication cost, computational cost and scalability. Furthermore, it identifies emerging challenges and suggests promising research directions in the federated learning domain.

The widespread adoption of smart technologies and digital services has intensified the demand for reliable and secure identity authentication systems. Biometric credentials, offering significant advantages over traditional methods, have emerged as a preferred solution. However, preserving the privacy of biometric data remains a critical challenge. This paper presents a comprehensive study of privacy-preserving techniques and potential risks associated with biometric systems. A novel taxonomy and structured framework are introduced to classify existing research in this domain. The study evaluates state-of-the-art approaches across a wide range of biometric modalities, including facial recognition, fingerprint analysis, eye-based identification, Electroencephalogram (EEG), Electrocardiogram (ECG), voice recognition, and hand geometry, highlighting their respective benefits, limitations, and application areas. Additionally, the paper examines the balance between privacy, security, and recognition performance, addressing current challenges and proposing future research directions. This work serves as a valuable resource for researchers and industry professionals, supporting the development of privacy-centric and secure biometric authentication technologies in an evolving digital landscape.

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 Face Recognition System is a remarkable process that humans naturally use, as a precursor to communicating nonverbal cues in their normal daily lives. Outside of this regime, face recognition systems are increasingly being used for recognizing faces in commercial and law enforcement applications. Currently, it is one of the most sought-after detection methods used in forensics for criminal identification purposes. Owing to similarities in the appearance of certain faces, face recognition systems today face substantial challenges in reliably identifying them in settings that require high detection rates. In addition, among criminal suspects found to have similar faces, this problem poses a great challenge in implementing forensic illegal and fraudulent activity detection. This study addresses the development of a detection pipeline that is data-driven, and fast, and incorporates structural information to overcome some of these issues. The novelty of this study lies in reconstructing a three-dimensional face mesh from two-dimensional images, of located 468 landmarks employing the media pipe framework. This two-dimensional to three-dimensional annotation provides a higher quality of three-dimensional reconstructed face models without the need for any additional three-dimensional morphable models. The proposed approach works better for detecting multiple faces in real-time and even in challenging uncontrolled conditions such as large pose variations, expression variations, and occlusion variations. The dataset consists of facial images collected from the web which provides the correct matching decisions in an unconstrained environment that is then used in forensic sciences to be presented as statistical evidence. Quantitative similarity measures are used as inputs for various classifiers to identify criminals in forensic investigations. The proposed methods were validated and tested to achieve comparable recognition performance and hint at the potential of further research for scale-up implementation.

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.