Convolutional Neural Networks for Biometrics Applications

Abstract

A convolutional neural network (CNN) is a feed-forward neural network that can react with other units in a specific range and can handle huge images well as a deep learning algorithm. CNN is a very convenient tool for conveying visual information and can be good for improving recognition accuracy. However, volumetric neural networks also increase the complexity of the networks, making them more challenging to optimize and more prone to overfitting. This paper will focus on the history of CNN development and the current use of the method, and the difficulties encountered. Furthermore, we will analyze its application in bioinformatics by discussing the papers published in the field about CNN. After the CNN was invented by Leon O. Chua and Lee Yang in 1988, researchers transformed a neural network into a CPU with thousands of cores. Improvements to the CNN in recent years have been made in six main parts: convolutional layer, pooling layer, activation function, loss function, regularization, and optimization, which have reduced the redundancy of the CNN and allowed it to process faster and more accurately processing. Nowadays, it is mainly used for image classification, text processing, video processing, etc. Above all, this paper realizes that CNN has excellent advantages in feature extraction and can play a huge role in dealing with eye biometrics, flower recognition, etc.