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Biometric technology is rapidly growing due to the urgent need to secure people’s properties, from goods to information, in the overwhelming digital technology proliferation in all aspects of society. In this paper, biometric recognition is defined as the automated recognition of individuals based on biological or behavioral characteristics, such as fingerprints, facial recognition, and speech patterns. The authors emphasize that a robust biometric system consists of a combination of physiological and behavioral features. However, using biometrics for identification raises privacy concerns and the paper addresses the need to balance privacy and security. A comprehensive section on biometric template protection is introduced to address biometrics privacy and different attack protections. It discusses deep neural network-based models to segment real-world features and match them for authentication. It presents a case study of a new model based on the Siamese neural network. It explains how the Siamese neural network can be used for biometric recognition and how it compares to other deep learning models commonly used in the field. Lastly, the paper discusses state-of-the-art methods to secure information and provides a futuristic view of the technology. This paper provides a comprehensive overview of biometric technology, its advantages, and the associated privacy concerns.