Palmprint Recognition Based on Residual Network and Improved Attention Mechanism
In the field of criminal investigation,automatic species identification for offline palmprint im-age(scanned image of palm print)is a necessary work to determine the location of the palm from which the palm print originates.Since offline palmprint involves personal privacy and limited access,there are few open and reliable offline palmprint databases.At present,there is a lack of systematic research on automatic species identification of offline palmprint.Based on this,3D IA-ResNet is proposed for left and right palmprint recognition.Firstly,the effect of the proposed improved attention module was verified on the Kaggle cat and dog dataset,and then the 3D IA-ResNet was used to realize left and right offline palm-print recognition on the self-built offline palm-print dataset.3D IA-ResNet performed well in ablation ex-periments and model stability experiments on Kaggle cat and dog recognition datasets,and the accuracy,recall rate and F1 value on left and right offline palmprint recognition datasets were as high as 96.6％,95.7％and 96.1％,which were 0.5％,0.7％and 0.6％higher than the baseline model.After the test of cat and dog recognition tasks,3D IA-ResNet effectively realizes the left and right hand offline palm-print recognition task,fills the research gap in related fields,and provides necessary technical support for complex palmprint recognition tasks.
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