© 2018 Mohamed Sayed and Faris Baker. This open access article is distributed under a Creative Commons Attribution (CC- BY) 3.0 license. Journal of Computer Science Original Research Paper Thermal Face Authentication with Convolutional Neural Network Mohamed Sayed and Faris Baker Faculty of Computer Studies, Arab Open University, Kuwait Article history Received: 25-10-2018 Revised: 10-11-2018 Accepted: 08-12-2018 Corresponding Author: Mohamed Sayed Faculty of Computer Studies, Arab Open University, Kuwait Email: msayed@aou.edu.kw 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. Keywords: Deep Learning, Convolutional Neural Networks, Image Processing, Face Recognition, Thermal Images Introduction Recognitions with a thermal camera is a challenge that have been recently improved by adopting deep learning methods using Convolutional Neural Networks (CNNs). Thermal camera forms a picture by capturing various heat levels emitted from objects. One of the applications of visualizing and identifying objects is face recognition. There are many useful applications for face recognition; an important one is an application for security at night where an intruder needs to be identified in the absence of light. Furthermore, it can be used as an additional tool to normal cameras for identification. Ensemble method of using both thermal and normal cameras adds more certainty weight to the proof of identity. In this study, faces are identified in different circumstances such as full light, partial light and dim light as well as in different emotional status, all based on the heat emitted from them rather than the light intensities reflected from them; the technology also allows us to measure the accuracy of the recognition using CNNs. Image processing (Nixon and Aguado, 2002) is a technique that is used to change over a picture into an enhanced computerized shape. It plays out a few operations together with a specific end goal to get an improved picture or to concentrate some valuable data from it. It is also a sort of flag regulation which information is a picture similar to a video edge or photo and might yield a picture or attributes related with that picture. Typically, image-processing framework incorporates pictures as two-dimensional signs while effectively applying strategies of set flag handling to them. It is among quickly developing advances today together with its applications in different parts of a business. Advanced processing methods help in controlling the computerized pictures by utilizing personal computers. The three general stages that a wide range of information needs to experience while utilizing computerized system are pre-handling, improvement and show and data extraction. CNNs are a special kind of multi-layer neural networks trained with a back-propagation algorithm that extracts important features, supplemented with filters that prevent overfitting and eliminate noise modelling. While a fully connected layer has a high cost of parameters and high risks of overfitting, the convolutional layer is more suitable for 2D spatial information because it captures the spatial characteristics