Harnessing Deep Learning Techniques for the Removal of Occlusion in Face Image

Occlusion removal is one of the challenging tasks in face recognition system. Facial occlusion is nothing but face object is hiding the other object which is one important factor that influences how well facial recognition works. There is insufficient research regarding the issue of occlusion. Researchers proposed several methods for removal of occlusion to recognize faces. But are unable to produce results that are realistic for the removal of significant objects, particularly in images of faces. The performance test of face recognizer recognition system degrades when dealing with masked faces. The objective of this research work to remove mask object from face images to recognizes faces easily. We divide the problem into two modules: occlusion detection module and occlusion removal module. We proposed deep learning-based technique called GAN-based network. Two discriminators are used in a GAN-based network; one discriminator helps in learning the face’s global structure, while the other learns the deep missing region. MaskedFace-CelebA dataset is used to evaluate our model that is utilised to compare masked images in pairs with ground truth in order to verify faces. The evaluation metric used in this research is SSIM. Using the proposed method can be useful task to generate new unmasked face samples and help criminal investigation agencies1to reveal the identity of criminals who committed the crimes behind the mask. Once the occlusion of face is removed, the system can easily recognize the face and the biometric authentication during COVID-19 pandemic no need to remove masks while taking biometric attendance, this will reduce the spread of COVID-19 disease.