Work

PBNet: Combining Transformer and CNN in Passport Background Texture Printing Image Classification

Year: 2024

Type: article

Abstract: Passport background texture classification has always been an important task in border checks. Current manual methods struggle to achieve satisfactory results in terms of consistency and stability for weakly textured background images. For this reason, this study designs and develops a CNN and Transformer complementary network (PBNet) for passport background texture image classification. We first design two encoders by Transformer and CNN to produce complementary features in the Transformer and CNN domains, respectively. Then, we cross-wisely concatenate these complementary features to propose a feature enhancement module (FEM) for effectively blending them. In addition, we introduce focal loss to relieve the overfitting problem caused by data imbalance. Experimental results show that our PBNet significantly surpasses the state-of-the-art image segmentation models based on CNNs, Transformers, and even Transformer and CNN combined models designed for passport background texture image classification.

Source: Electronics

DOI: 10.3390/electronics13214160

Authors: Jiafeng Xu, Dawei Jia, Zhizhe Lin, Teng Zhou, Jie Wu, Lin Tang

Institutions: China People's Police University, Hainan University, University of Electronic Science and Technology of China, Henan University, Chengdu University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

OpenAlex ID: W4403683080

FWCI: 0.14

Cites: 45

Cited by: 1

Related to: 10