Performance of face detection and recognition is affected and damaged because occlusion often leads to missed detection. To reduce the recognition accuracy caused by facial occlusion and enhance the accuracy of face detection, a visual attention mechanism guidance model is proposed in this paper, which uses the visual attention mechanism to guide the model highlight the visible area of the occluded face; the face detection problem is simplified into the high-level semantic feature detection problem through the improved analytical network, and the location and scale of the face are predicted by the activation map to avoid additional parameter settings. A large number of simulation experiment results show that our proposed method is superior to other comparison algorithms for the accuracy of occlusion face detection and recognition on the face database. In addition, our proposed method achieves a better balance between detection accuracy and speed, which can be used in the field of security surveillance.

中文翻译：

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基于视觉注意力机制制导模型的非受限姿势下的人脸检测与识别

人脸检测和识别的性能受到影响和损坏，因为遮挡往往会导致漏检。为了降低人脸遮挡导致的识别准确率，提高人脸检测的准确率，本文提出了一种视觉注意机制引导模型，利用视觉注意机制引导模型突出被遮挡人脸的可见区域；通过改进的分析网络将人脸检测问题简化为高级语义特征检测问题，通过激活图预测人脸的位置和尺度，避免额外的参数设置。大量的仿真实验结果表明，我们提出的方法在人脸数据库上遮挡人脸检测和识别的准确性方面优于其他比较算法。此外，我们提出的方法在检测精度和速度之间取得了更好的平衡，可用于安全监控领域。