Multi-Scale Region of Interest Feature Fusion for Palmprint Recognition

MA Yuxuan; ZHANG Feifei; LI Guanghui; TANG Xin; DONG Zhengyang

doi:10.11999/JEIT250940

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MA Yuxuan, ZHANG Feifei, LI Guanghui, TANG Xin, DONG Zhengyang. Multi-Scale Region of Interest Feature Fusion for Palmprint Recognition[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250940

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MA Yuxuan, ZHANG Feifei, LI Guanghui, TANG Xin, DONG Zhengyang. Multi-Scale Region of Interest Feature Fusion for Palmprint Recognition[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250940

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Multi-Scale Region of Interest Feature Fusion for Palmprint Recognition

doi: 10.11999/JEIT250940 cstr: 32379.14.JEIT250940

1.
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
2.
Jiangsu Bangrong Microelectronics Co., Ltd., Suzhou 215000, China

Funds: The National Natural Science Foundation of China (62372214), Suzhou Science and Technology Project (SGC2021070)

Received Date: 2025-09-22
Accepted Date: 2025-12-30
Rev Recd Date: 2025-12-30

Available Online: 2026-01-08

Abstract

Abstract

Objective Accurate localization of the Region Of Interest (ROI) is a prerequisite for high-precision palmprint recognition. In contactless and uncontrolled application scenarios, complex background illumination and diverse hand postures frequently cause ROI localization offsets. Most existing deep learning-based recognition methods rely on a single fixed-size ROI as input. Although some approaches adopt multi-scale convolution kernels, fusion at the ROI level is not performed, which makes these methods highly sensitive to localization errors. Therefore, small deviations in ROI extraction often result in severe performance degradation, which restricts practical deployment. To overcome this limitation, a Multi-scale ROI Feature Fusion Mechanism is proposed, and a corresponding model, termed ROI3Net, is designed. The objective is to construct a recognition system that is inherently robust to localization errors by integrating complementary information from multiple ROI scales. This strategy reinforces shared intrinsic texture features while suppressing scale-specific noise introduced by positioning inaccuracies. Methods The proposed ROI3Net adopts a dual-branch architecture consisting of a Feature Extraction Network and a lightweight Weight Prediction Network (Fig. 4). The Feature Extraction Network employs a sequence of Multi-Scale Residual Blocks (MSRBs) to process ROIs at three progressive scales (1.00×, 1.25×, and 1.50×) in parallel. Within each MSRB, dense connections are applied to promote feature reuse and reduce information loss (Eq. 3). Convolutional Block Attention Modules (CBAMs) are incorporated to adaptively refine features in both the channel and spatial dimensions. The Weight Prediction Network is implemented as an end-to-end lightweight module. It takes raw ROI images as input and processes them using a serialized convolutional structure (Conv2d-BN-GELU-MaxPool), followed by a Multi-Layer Perceptron (MLP) head, to predict a dynamic weight vector for each scale. This subnetwork is optimized for efficiency, containing 2.38 million parameters, which accounts for approximately 6.2% of the total model parameters, and requiring 103.2 MFLOPs, which corresponds to approximately 2.1% of the total computational cost. The final feature representation is obtained through a weighted summation of multi-scale features (Eq. 1 and Eq. 2), which mathematically maximizes the information entropy of the fused feature vector. Results and Discussions Experiments are conducted on six public palmprint datasets: IITD, MPD, NTU-CP, REST, CASIA, and BMPD. Under ideal conditions with accurate ROI localization, ROI3Net demonstrates superior performance compared with state-of-the-art single-scale models. For instance, a Rank-1 accuracy of 99.90% is achieved on the NTU-CP dataset, and a Rank-1 accuracy of 90.17% is achieved on the challenging REST dataset (Table 1). Model robustness is further evaluated by introducing a random 10% localization offset. Under this condition, conventional models exhibit substantial performance degradation. For example, the Equal Error Rate (EER) of the CO3Net model on NTU-CP increases from 2.54% to 15.66%. In contrast, ROI3Net maintains stable performance, with the EER increasing only from 1.96% to 5.01% (Fig. 7, Table 2). The effect of affine transformations, including rotation (±30°) and scaling (0.85$ \sim $1.15×), is also analyzed. Rotation causes feature distortion because standard convolution operations lack rotation invariance, whereas the proposed multi-scale mechanism effectively compensates for translation errors by expanding the receptive field (Table 3). Generalization experiments further confirm that embedding this mechanism into existing models, including CCNet, CO3Net, and RLANN, significantly improves robustness (Table 6). In terms of efficiency, although the theoretical computational load increases by approximately 150%, the actual GPU inference time increases by only about 20% (6.48 ms) because the multi-scale branches are processed independently and in parallel (Table 7). Conclusions A Multi-scale ROI Feature Fusion Mechanism is presented to reduce the sensitivity of palmprint recognition systems to localization errors. By employing a lightweight Weight Prediction Network to adaptively fuse features extracted from different ROI scales, the proposed ROI3Net effectively combines fine-grained texture details with global semantic information. Experimental results confirm that this approach significantly improves robustness to translation errors by recovering truncated texture information, whereas the efficient design of the Weight Prediction Network limits computational overhead. The proposed mechanism also exhibits strong generalization ability when integrated into different backbone networks. This study provides a practical and resilient solution for palmprint recognition in unconstrained environments. Future work will explore non-linear fusion strategies, such as graph neural networks, to further exploit cross-scale feature interactions.
- Palmprint recognition,
- Multi-scale feature fusion,
- Deep learning

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References(22)

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