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      Biometric Identification Using Deep Learning for Advance Cloud Security

       Navani Siroya1* and Manju Mandot2

       1 MDS University Ajmer, India

       2 Computer Science, JRN Rajasthan Vidyapeeth University, Udaipur, India

      * Corresponding author: [email protected]

       Abstract

      A few decades ago, biometric identification was a staple technology of highly advanced security systems in movies, but today, it exists all around us. Biometric technologies have the potential to revolutionize approaches to identity verification worldwide.

      This chapter discusses the prevailing Biometric modalities, their classification, and their working. It goes on to discuss the various approaches used for Facial Biometric Identification such as feature selection, extraction, face marking, and the Nearest Neighbor Approach.

      Here, we propose a system that compares an input image with that of the database in order to detect the presence of any similarities. Moreover, we use fiducially point analysis to extract facial landmarks and compare them with the database using data mining and use the Nearest Neighbor Approach for identifying similar images.

      The chapter ends with deliberations on the future extent of Biometric technologies and the need to put in ample safeguards for data protection and privacy.

      Keywords: Biometric, feature extraction, facial recognition, nearest neighbor approach

1.1 Introduction

      Biometric authentication is a security process that relies on the unique biological characteristics of a person in order to affirm their identity. Biometric verification frameworks compare biometric data with existing original datasets that are stored. Examples of biometric characteristics include iris, palm print, retina, fingerprint, face, and voice signature. In recent years, deep learning-based models have helped accomplish best in class results in machine vision, audio recognition, and natural language processing tasks. These models appear to be a characteristic fit for dealing with the everexpanding size of biometric acknowledgment issues, from phone verification to air terminal security frameworks. Thus, application of machine learning techniques to biometric security arrangements has become a trend [1].

      Classification of Biometric Data:

      Data science consultants can use machine learning’s capacity to mine, look, and examine huge datasets for improving the execution of security frameworks and their reliability.

In light of its exceptional capacity to recognize people, biometric innovation has quickly become a way to help forestall shams and discovered its place in today’s standard advancements. Consequently, it turns out to be more reliable than the customary validation frameworks that utilize passwords and documents for verification shown in Figure 1.1.