A Review on Deep Learning Techniques for Preserving User Privacy through Biometrics

  • Mrs. J. Samatha, Dr. G. Madhavi

Abstract

Deep learning (DL) is playing an ever more important function in our lives. It has already made an enormous impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition. Deep neural network (DNN) uses multiple (deep) layers of units with highly optimized algorithms and architectures. Deep learning uses multiple layers to represent the notion of data to build computational models. In recent times, authentication and identification of a person has become an essential part of most of the computer vision automation systems. But the usage of passwords and tokens becomes highly insecure or forgotten. To overcome this problem biometric systems have been intensively researched and widely applied in the last decade. This paper considered a detailed review on different types of deep learning techniques and their applications for image analysis of biometrics for preserving user privacy. A critical review has been carried out throughout the paper to understand the current state-of-the-art for selecting an appropriate direction for future research.

Published
2021-10-20
How to Cite
Mrs. J. Samatha, Dr. G. Madhavi. (2021). A Review on Deep Learning Techniques for Preserving User Privacy through Biometrics. Design Engineering, 5702 - 5715. Retrieved from http://www.thedesignengineering.com/index.php/DE/article/view/5530
Section
Articles