Neural Network Optimization-Based Facial Geometric Key Homomorphic Cloud Security
Keywords:
Encryption, Facial Features, Algorithm, RecognitionAbstract
In the modern day, cloud computing has become an essential component of technological and individual interactions with computing devices. The majority of cloud platforms use conventional encryption techniques for device authentication, whereas the cloud services that are still useful offer consumers older protocol for device authentication. The face authentication protocol described in this work will provide a facial geometric point that will serve as the encryption key. The method was created as a multi-stage implementation of the interdependent facial recognition algorithms. The fuzzy neural inference algorithm, which forms the basis of the first sequence of the algorithm, is used to implement face verification of users and the input of each person into a tracking sheet. The second method involved facial geometric point mapping of a face for the recognition of deep facial features using a convolution neural network (CNN) using a VGG19-based architecture. A facial geometric point-based facial network was created based on the algorithm, and each unique face was given a label. The process added the ability to recognize numerous faces in a single image. The third approach makes use of a cloud authentication system that is based on face geometric point identification. Based on facial geometry points that will be used as input for the encryption cypher, this method generates a dynamic encryption key. In order to encrypt the file, these facial geometric points, which are calculated based on the number of regions that were detected on a particular face, are given a dynamically assigned value and input to the key of the specific algorithm. These geometric points will also be used to decrypt the file.
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