Author(s)

Xueqiu Dong, Liye Mei, And Junhua Zhang

Corresponding Author

Xueqiu Dong

ABSTRACT

Palmprint recognition, as one of the current biometric technologies, has received extensive attention and research. There are difficulties in the traditional artificially defined feature when it extracts problems. In order to identify palmprints simply and efficiently, meanwhile, to eliminate the problem of coupling palmprint recognition with manually defined feature extraction problems, an algorithm for palmprint recognition directly on palmprints is proposed. The ability of automatic feature extraction utilizing convolution network is used to learn the palmprint features from the palmprint database, and the network can adapt to the variety of the palmprint by the design of the training data. The experimental results show that the palmprint recognition approach based on the convolution neural network (CNN) achieves an overall accuracy of 99.95%, and the model parameters are reduced by a lot compared with those in the standard network model. Its accuracy is much better than that of traditional palmprint recognition algorithm.

KEYWORDS

Palmprint Recognition, Feature Extraction, Convolutional Neural Network