ResNet18 facial feature extraction algorithm improved based on hybrid domain attention mechanism - Yingying Mei (PLoS ONE, 2025)
In the research of face recognition technology, the traditional methods usually show poor recognition accuracy and insufficient generalization ability when faced with complex scenes such as lighting changes, posture changes and skin color diversity. To solve these problems, based on the improvement of adaptive boosting to improve the accuracy of face detection, the study proposes a residual network 18-layer face feature extraction algorithm based on hybrid domain attention mechanism algorithm. The study introduces channel-domain and spatial-domain attention mechanism to enhance the extraction of face image features. The outcomes indicated that the recognition accuracy of the proposed method on multiple face image datasets, labeled field face datasets, and celebrity facial attribute datasets exceeded 98.34% and reached up to 99.64%, which was better than the current state-of-the-art methods. After combining channel and spatial attention mechanism, the false detection rate was as low as 2.50%, which was lower than the false detection rate of other methods. In addition to enhancing face recognition’s robustness and accuracy, the work offers fresh concepts and resources for face recognition’s potential uses in intricate scenarios in the future.