Results of ablation experiment.

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.

在人脸识别技术的研究中，传统方法在面对光照变化、姿态变化以及肤色多样性等复杂场景时，往往识别精度欠佳且泛化能力不足。为解决上述问题，本研究在改进自适应提升（Adaptive Boosting）算法以提升人脸检测精度的基础上，提出了一种基于混合域注意力机制（hybrid domain attention mechanism）的18层残差神经网络（residual network 18-layer）人脸特征提取算法。该研究引入通道域与空间域注意力机制，以增强人脸图像特征的提取效果。实验结果表明，所提方法在多个人脸图像数据集、带标注的野外人脸数据集以及名人面部属性数据集上的识别精度均超过98.34%，最高可达99.64%，性能优于当前最先进（state-of-the-art）方法。结合通道与空间注意力机制后，其误检率低至2.50%，低于其他对比方法的误检率。除了提升人脸识别的鲁棒性与精度外，本研究还为未来人脸识别在复杂场景中的潜在应用提供了全新的思路与参考资源。