Comprehensive Analysis of Hand Radiographic based Person Authentication

One of the most important components of the majority of computer vision automation systems is human authentication. It is impossible for conventional systems that are based on fingerprints, iris, face, and palm prints to identify a human being when these exterior biometric elements are destroyed due to things like rashes, wounds, severe burns, and other similar factors. The most important characteristics of any person authentication system are its security, resilience, privacy, and non-forgery capabilities. A comprehensive overview of numerous methods of person authentication based on hand radiographs is presented in this work. The technique, radiographic modality employed, dataset, benefits, and downsides of these methods are some of the topics that are discussed. It fills in the study gaps that were discovered during the survey and throws up the door to the possibility of future improvements to the authenticity of individuals based on radiography. Additionally the effectiveness of the system is analyzed for the different machine learning algorithms such as support vector machine (SVM), K-nearest neighbor (KNN), classification tree (CT) and random forest (RF).