Rashmi Adyapady, R.Annappa, B.2026-02-0620232023 IEEE 8th International Conference for Convergence in Technology, I2CT 2023, 2023, Vol., , p. -https://doi.org/10.1109/I2CT57861.2023.10126182https://idr.nitk.ac.in/handle/123456789/29554Occlusions 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. © 2023 IEEE.Facial OcclusionsOcclusion DetectionResidual AttentionXception Network