A Survey on Deep Learning Techniques for Big Data in Biometrics

Abstract

Big Data and deep learning are two important words in data science now days. The large volumes of data collected by organizations are utilized for various purposes such as for solving problems in marketing, technology, medical science, national intelligence, fraud detection etc. Traditional data processing systems are not adequate to handle, analyze and process as the collected data are unlabelled, uncategorized and very complex. Hence deep learning algorithms which are specialized in analysing such large volumes of unsupervised data can be utilized. The key characteristic that makes deep learning tools the most suitable ones for big data analytics is that they continuously improvise with each set of data they tackle. Deep learning is appropriate for exploiting large volumes of data and for analysing raw data from multiple sources and in different styles. This paper presents an overview of different deep learning techniques for big data in biometrics and discusses some issues and solutions.