| dc.description.abstract | In an age when people are more and more concerned about personal privacy protection,
all parties are working hard to conceive new ways of identity authentication to ensure that the
user′s identity can be verified and will not be stolen. Therefore, this paper investigates the
application of deep learning neural networks to multi-modal biometric recognition of multispectral palm images in order to improve the accuracy of body recognition. In this paper, we
use the CASIA multi-spectral palm image dataset as the basis of the experiment. Each palm
image of each spectrum contains multi-modal biometric features such as palm print, hand shape,
knuckle pattern, and fingerprint, etc. The multi-modal biometric features of the palm are input
into the deep learning network, and each biometric feature of the palm image is applied to
improve the accuracy of recognition.
In order to effectively improve the accuracy of the trained models, we first tried to train and
test the unpre-trained network models and found that the results were not as good as expected;
we introduced the pre-trained models as a way to improve the accuracy of recognition, and the
accuracy obtained after training increased significantly. In the experiments, data augmentation
was performed using common camera environment changes such as random rotation, panning,
and luminance changes, and it was found that this not only enhanced the accuracy of the model,
but also enhanced the ability of the model to adapt to image changes. The experimental results
validate the potential of using multispectral palm multimodal features for body recognition by
using appropriate pre-training deep learning neural network models and increasing the amount
of data enhancement. | en_US |