Thermal to Visible Face Recognition Using Deep Autoencoders. Visible face recognition systems achieve nearly perfect recognition accuracies using deep learning. However, in lack of light, these systems perform poorly. A way to deal with this problem is thermal to visible crossdomain face matching. This is a desired technology because of its usefulness in night time surveillance. Nevertheless, due to differences between two domains, it is a very challenging face recognition problem. In this paper, we present a deep autoencoder based system to learn the mapping between visible and thermal face images. Also, we assess the impact of alignment in thermal to visible face recognition. For this purpose, we manually annotate the facial landmarks on the Carl and EURECOM datasets. The proposed approach is extensively tested on three publicly available datasets: Carl, UNDX1, and EURECOM. Experimental results show that the proposed approach improves the stateoftheart significantly. We observe that alignment increases the performance by around 2%. Annotated facial landmark positions in this study can be downloaded from the following link: github.comAlpkantThermaltoVisibleFaceRecognitionUsingDeepAutoencoders .

Bring Light to the Night: Classifying Thermal Image Via Convolutional Neural Network Based on Visible Domain Transformation Most existing vision systems target at processing images captured during the day time. However, it is also essential to enable cameras to see the scenes during the night, such as in outdoor places where no light exists and power outage in indoor environments. We capture thermal images to observe objects in the dark environment. Based on the captured thermal images, we develop a convolutional neural network to classify the images. As thermal images require to invest a substantial amount of time to create clear images, we also rely on color images to enrich the training samples and apply transfer learning to refine the CNN classification models. The visible source domain network is learned together with a decoding network to enforce the source domain learning outcome resembling the target thermal domain properties.