Face Mask Detector is used to automate the detection of face mask using images captured from a thermal camera. The problem is posed as a binary classification problem, wherein the input face image needs to be classified as with mask or without mask. Transfer learning is used for classification, wherein deep CNN model, MobileNetV2, is trained on a dataset of thermal face images with mask and without mask. The steps for building model are collecting the data, pre-processing, split the data, training the model, testing the model, and implement the model. The dataset is prepared using lepton FLIR camera interfaced to a Raspberry pi board. The built model can now detect people who are wearing a face mask and not wearing with an accuracy of 97 percent.