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In this project, palmprint recognition requires extraction of palmprint features before classification and recognition, which will affect the recognition rate.
To solve this problem, this paper uses the convolutional neural network (CNN) structure Densenet to realize palmprint recognition.
First, according to the characteristics of the geometric shape of palmprint, the ROI area of palmprint was cut out. Then the ROI area after processing is taken as input of convolutional neural network.
Next the PRelu activation function is used to train the network to select the best learning rate and super parameters. Finally, the palmprint was classified and identified.