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.
These last years, we have witnessed considerable improvements in machine learning and deep learning. Many advanced techniques are now based on deep neural networks. Although many software libraries are available, the development of deep neural networks requires a good level of mathematical knowledge and high programming skills. In this work, we present a visual tool to help simplify the programming of deep learning networks. The developed framework DeepViP is comprised of a node editor that provides users with a toolbox representing different types of neural layers. It allows the connection between the different blocks and the configuration of important hyper parameters of each layer. Thus, speeding-up experimentation with different architectures. Additionally, the developed solution offers users the possibility to generate a python script of the designed network that can be run using specific libraries such as keras or tensorflow.