The detection of masked face is becoming an essential part of health care safetydue to the pandemic caused by the coronavirus and the surveillance systems. One of the most challenging problems in face recognition systems is the accurate identification of faces in the presence of occlusion like wearing of glasses and masks. The current study proposes a novel convolutional neural network(CNN)-based model for accurate detection of faces in the presence of mask and glasses.The novel architecture of the model was developed using ten convolutional layers, five max-pooling layers, and a dropout layer. The Adam optimizer was used for optimization of the performance our model. Early stopping criteria in conjunction with the ReduceLROnPlateau class was employed to avoid the overfitting problem. Our proposed model could achieve the accuracy of 99.71% on the test dataset suggesting its superiorityto its existing counterparts. Based on the results, the suitability of the proposed model for face detection in the presence of occlution in real-life application has been recommended.

Deep learning, convolutional neural network, facial recognition, image processing , Occluded Faces with Mask, Occluded Faces with Glasses