In recent years, pattern recognition has garnered widespread attention, especially in the domain of face recognition. Traditional face recognition methods have certain limitations in unconstrained environments due to factors such as lighting, facial expressions, and poses. Deep learning can be used to address these challenges. This paper proposes a comprehensive approach to face occlusion recognition based on a two-stage Convolutional Neural Network (CNN). Face verification aims at verifying whether two face images belong to the same individual, and it is a more fundamental task compared to face recognition. The process of face recognition essentially involves multiple instances of face verification, sequentially validating different individuals to ultimately determine the corresponding individual for each face. The primary steps in this research include facial detection, image preprocessing, facial landmark localization, facial landmark extraction, feature matching recognition, and 2D image-assisted 3D face reconstruction. A novel two-stage CNN was designed for facial detection and alignment. The first stage of the network is dedicated to the search for facial windows and regressing vector boundaries. The second stage utilizes 2D images to assist in 3D face reconstruction and perform secondary recognition for cases not identified in the first stage. This method demonstrated excellent performance in handling facial occlusions, achieving high accuracy on datasets such as AFW and FDDB. On the test dataset, face recognition accuracy reached 97.3%, surpassing the original network accuracy of 89.1%. This method outperforms traditional algorithms and general CNN approaches. This study achieved efficient face validation and further handling of unrecognized situations, contributing to the enhancement of face recognition system performance.
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