Thermal Face Authentication with Convolutional Neural Network

Abstract

Matching thermal face images as a method of biometric authentication has gained increasing interest because of its advantage of tracking a target object at night and in total darkness. Therefore, for security purposes, it has become highly favourable and has extensive applications, for instance, in video surveillance at night. The aim of this study is to present a simple and efficient deep learning model, which accurately predicts person identification. A pre-trained Convolutional Neural Network (CNN) is employed to extract the features of the multiple convolution layers of the low resolutions’ thermal infrared images. To run the program and evaluate the performance, we use a sample of 1500 resized thermal images, each with resolution 181×161 pixels. The sample comprises of images that were captured within different time-lapse and with diverse emotions, poses and lighting conditions. The proposed approach is effective compared to the state-of-the-art thermal face recognition algorithms and achieves impressive accuracy of 99.6% with less processing and training times.