Detection of partially occluded area in face image using U-Net model

Abstrak

Occluded face recognition is important task in computer vision. To complete the occluded face recognition efficiently, first we need to identify the occluded region in face. Identifying the occluded region in face is a challenging task in computer vision. One case of face occlusion is nothing but wearing masks, sunglasses, and scarves. Another case of face occlusion is face is hiding the other objects like books, things, or other faces. In our research, identifying the occluded area which is corona virus disease of 2019 (COVID-19) masked area in face and generate segmentation map. In semantic segmentation, deep learning-based techniques have demonstrated promising outcomes. We have employed one of the deep learning-based U-Net models to generate a binary segmentation map on masked region of a human face. It achieves reliable performance and reducing network complexity. We train our model on MaskedFace-CelebA dataset and accuracy is 97.7%. Results from experiments demonstrate that, in comparison to the most advanced semantic segmentation models, our approach achieves a promising compromise between segmentation accuracy and computing efficiency.