Capsule network has shown excellent performance in many fields, but there is not much exploration of capsule network in face detection and recognition. For traditional CNN on face detection identification problem such as characteristic information lost and too dependent on sample, a capsule network with an iterative routing mechanism is used and improved upon it. Based on this, we introduce a two-layer feature extraction layer, and then add a spatial attention mechanism in the pooling process, so that the network pays more attention to the contour information of the face and retains the features of key areas of the face to the greatest extent. In order to increase the accuracy of the recognition algorithm, the open-source tool called “MaskTheFace” is employed to create a dataset of one-to-one correspondence between faces and occluded faces. Through the experiment, it can be seen that the improved algorithm has better performance than the traditional algorithm. Although the study is exploratory, it provides some insights into masked face identification.
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