Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection

TL;DR

This paper reviews how Recurrent Neural Networks, especially LSTM and Deep-Residual architectures, are applied to biometric authentication, anomaly detection, and other fields, analyzing their methodologies, benefits, and limitations.

Contribution

It provides a comprehensive review of RNN applications across multiple domains, highlighting the strengths and weaknesses of different architectures and methodologies.

Findings

RNNs are effective for biometric authentication and anomaly detection.

Different RNN architectures have specific advantages depending on the application.

The paper discusses the benefits and drawbacks of various RNN models.

Abstract

Recurrent Neural Networks are powerful machine learning frameworks that allow for data to be saved and referenced in a temporal sequence. This opens many new possibilities in fields such as handwriting analysis and speech recognition. This paper seeks to explore current research being conducted on RNNs in four very important areas, being biometric authentication, expression recognition, anomaly detection, and applications to aircraft. This paper reviews the methodologies, purpose, results, and the benefits and drawbacks of each proposed method below. These various methodologies all focus on how they can leverage distinct RNN architectures such as the popular Long Short-Term Memory (LSTM) RNN or a Deep-Residual RNN. This paper also examines which frameworks work best in certain situations, and the advantages and disadvantages of each pro-posed model.