Detection and Localization of Mask Occluded Faces by transfer learning using Faster RCNN

Here in this work we utilized the novel transfer learning based technique, where a pretrained network model weights are used to train our Faster Region Convolutional Neural Network (FRCNN). With the help of captured mask-face dataset, we further propose a masked face detection and recognition work. It incorporate three major network training modules. Proposed network module cascade two pre-trained CNNs to extract target facial features and strong region of interest (ROI) from the input dataset image for transformed domain represention with higher spatial descriptors. By similar method, omitted facial feature cues are sufficiently reconstructed and the distortion introduced by noise pixs cues by masks are reduced. Lastly, the pre-trained CNN module is used to detect and identify masked face regions of the image.The network estimates mask detection accuracy by putting bounding boxs around all the occluded mask face for performing the classification and line regression work. Experiments conducted on proposed dataset shows good accuracy and reduced running time overfew state-of-the-arts by 11.46 %.