Effective Removal of User-Selected Foreground Object From Facial Images Using a Novel GAN-Based Network

Abstract

This research features a user-friendly method for face de-occlusion in facial images where the user has control of which object to remove. Our system removes one object at a time, however, it is capable of removing multiple objects through repeated application. Although we show the effectiveness of our system on five commonly occurring occluding objects including hands, a medical mask, microphone, sunglasses, and eyeglasses, more types of object can be considered based on the proposed methodology. Our model learns to detect a user-selected, possibly distracting, object in the first stage. Then, the second stage removes the object using the object detection information from the first stage as guidance. To achieve this, we employ GAN-based networks in both stages. Specifically, in the second stage, we integrate both partial and vanilla convolution operations in the generator part of the GAN network. We show that by using this integration, the proposed network can learn a well-incorporated structure and also overcome the problem of visual discrepancies in the affected region of the face. To train our network, we produce a paired synthetic face-occluded dataset. Our model is evaluated using real world images collected from the Internet and publicly available CelebA and CelebA-HQ datasets. Experimental results confirm our model's effectiveness in removing challenging foreground non-face objects from facial images as compared to the existing representative state-of-the-art approaches.

Keywords

• Generative adversarial network
• object removal
• image editing
• image completion