| Summary: | Passport security feature classification has always been an important issue in border checks. Current manual methods struggle to achieve satisfactory results in terms of consistency and stability for security features with high similarity. For this reason, this study designs and develops a deep learning-based passport security feature classification model that can identify similar security features. The proposed model is based on the ShuffleNet v2, and its network structure is further optimized by two aspects for our task. Firstly, we embed pixel attention in the model to enable the network to better focus on important features with discriminatory power. Secondly, we introduce focal loss to relieve the overfitting problem caused by data imbalance. Finally, the superiority of the proposed classification algorithm is verified with the constructed passport security features data. The classification accuracy of the proposed algorithm is enhanced by 0.8%.The experimental results show that the classification accuracy is as high as 95.5%.
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