Adaptive deep convolutional neural network for thermal face recognition

An individual's face is a biometric feature that can be used in a computerized security system to identify or authenticate that particular person. The main challenge, while identifying a face through the use of a machine, is to match precisely the captured person's face with the image of the same individual's face already existing in the system's face database. Visual spectrum face images are affected by variations in lighting, head orientation, aging, and disguise resulting in poor visual face detection performance. Infrared imaging is used to help overcome some of these limitations. In this work, we propose a deep Deep Convolutional Neural Network architecture based on the FaceNet architecture and the MTCNN model to perform face recognition on a set of thermal data. Tests conducted on the USTC-NVIE dataset show promising results and the possibility of using deep learning in thermal face recognition.