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.
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