An Xception Model with Residual Attention Mechanism for Facial Occlusion Detection

Abstract

Occlusions occur due to the presence of obstacles. It poses difficulty in localizing and detecting the facial region, resulting in substantial intra-expression variability caused by noise and outliers. Facial occlusions are one of the most common issues that exist in real-world images. Solving such issues is essential for improving face recognition. The main aim of this work is to detect the occluded face. This work proposes a modified Xception network along with a residual attention mechanism to detect occluded parts of the facial region. The recognition accuracy obtained with the proposed Xception network with residual attention (Xcep-RA) mechanism is 99.97%, 99.85%, and 98.95% using Webface-OCC, Labeled Faces in the Wild (LFW), and Real-World Masked Face Dataset (RMFD) datasets. Extensive experiments using Xcep-RA significantly achieved competitive results compared to state-of-the-art methods on Webface-OCC, LFW, and RMFD datasets.