面向掌纹识别的多尺度感兴趣区域特征融合机制

马宇轩; 张飞飞; 李光辉; 唐鑫; 董正阳

doi:10.11999/JEIT250940

面向掌纹识别的多尺度感兴趣区域特征融合机制

doi: 10.11999/JEIT250940 cstr: 32379.14.JEIT250940

1.
江南大学人工智能与计算机学院无锡 214122
2.
江苏邦融微电子有限公司苏州 215300

基金项目: 国家自然科学基金(62372214)，苏州市科技计划(SGC2021070)

详细信息

作者简介:
马宇轩：男，硕士生，研究方向为生物特征识别、深度学习等

张飞飞：男，硕士，高级工程师，研究方向为图像处理算法的硬件加速和SoC芯片设计

李光辉：男，博士，教授，研究方向为物联网、边缘计算、无损检测、集成电路设计验证等

唐鑫：男，硕士生，研究方向为生物特征识别、深度学习等

董正阳：男，硕士生，研究方向为生物特征识别、人脸表情识别、深度学习等

通讯作者:
李光辉　ghli@jiangnan.edu.cn

中图分类号: TN911.73; TP391.4
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出版历程
- 收稿日期: 2025-09-22
- 修回日期: 2025-12-30
- 录用日期: 2025-12-30
- 网络出版日期: 2026-01-08
- 刊出日期: 2026-03-10

Multi-Scale Region of Interest Feature Fusion for Palmprint Recognition

1.
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
2.
Jiangsu Bangrong Microelectronics Co., Ltd., Suzhou 215300, China

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

摘要

摘要: 定位感兴趣区域(ROI)是掌纹识别流程中的关键环节，然而，在实际应用中，光照变化与手掌姿态的多样性常常导致ROI定位出现偏移，进而影响识别系统的性能。为缓解此问题，该文提出一种新颖的多尺度ROI特征融合机制，并据此设计了一个双分支协同工作的深度学习模型。该模型由特征提取网络和权重预测网络构成：前者负责从多个不同尺度的ROI中并行提取特征，后者则自适应地为各尺度特征分配权重。该融合机制的核心思想在于，不同尺度的ROI既共享了掌纹的核心纹理等本质特征，又各自包含了独特的尺度相关信息。通过对这些特征进行加权融合，模型能够强化共有的本质特征，同时抑制由定位不准引入的噪声和冗余信息，从而生成更具鲁棒性的特征。在IITD, MPD和NTU-CP等多个公开掌纹数据集上的综合实验表明，该模型在存在显著定位误差时，其识别精度仅出现小幅下降，展现出远超传统单尺度ROI模型的抗误差能力。特别是在NTU-CP定位误差测试中，该模型的等错误率(EER)仅从1.96%小幅上升至5.01%，而其他对比模型的EER均超过10%，这充分证实了所提多尺度ROI特征融合机制的有效性与优越性。
- 掌纹识别 /
- 多尺度特征融合 /
- 深度学习
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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图 1 多尺度ROI特征融合机制

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图 2 多尺度ROI掌纹

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图 3 不同尺度的ROI

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图 4 ROI3Net模型结构

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图 5 权重热力图

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图 6 正常定位下不同模型的ROC曲线

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图 7 存在定位错误下不同模型的ROC曲线

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表 1 正常定位下实验结果(%)

方法	IITD		MPD		NTU-CP		REST		CASIA		BMPD
方法	EER	Rank-1	EER	Rank-1	EER	Rank-1	EER	Rank-1	EER	Rank-1	EER	Rank-1
本文模型	3.60	99.00	4.97	99.90	1.96	99.90	8.59	90.17	1.21	99.90	6.51	99.88
CompNet	6.32	98.61	8.36	99.90	3.50	99.65	12.70	87.66	1.26	99.90	8.89	99.62
CCNet	5.67	99.00	7.46	99.86	2.63	99.65	10.84	86.89	1.31	99.90	8.77	99.75
CO3Net	5.73	99.00	8.47	99.82	2.54	99.74	13.63	84.29	1.84	99.77	10.21	99.75
DCPV	8.65	95.69	11.32	99.71	7.37	98.89	19.97	80.88	4.87	99.36	13.80	99.25
RLANN	4.68	99.00	7.18	99.78	2.78	99.48	16.13	82.27	1.77	99.80	11.01	99.62
PalmALNet	6.23	95.15	19.42	96.73	7.68	97.53	19.88	81.66	2.51	99.53	14.54	98.75
MTCC	5.57	97.62	8.71	99.72	4.42	99.57	16.05	82.93	2.37	99.80	13.91	99.38

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表 2 存在定位误差实验结果(%)

方法	IITD		MPD		NTU-CP		REST		CASIA		BMPD
方法	EER	Rank-1	EER	Rank-1	EER	Rank-1	EER	Rank-1	EER	Rank-1	EER	Rank-1
本文模型	10.15	90.53	6.33	99.58	5.01	96.76	11.76	87.21	2.70	99.43	10.60	99.32
CompNet	30.22	57.92	12.49	99.08	14.52	68.42	20.72	79.52	7.01	98.63	16.72	98.12
CCNet	27.60	61.76	12.86	97.96	10.11	80.93	18.76	81.37	6.00	98.73	15.98	97.12
CO3Net	28.36	61.76	13.56	97.96	15.66	58.55	22.26	75.27	8.10	97.09	17.73	96.37
DCPV	29.51	38.61	18.86	92.68	20.53	44.25	21.50	76.83	14.27	87.83	18.37	93.37
RLANN	14.16	75.84	9.98	98.82	11.36	75.40	19.87	80.62	3.21	99.13	16.81	97.00
PalmALNet	17.31	29.15	17.55	70.91	13.08	71.23	18.79	81.26	4.04	97.96	16.60	95.75
MTCC	22.68	30.08	11.83	99.20	13.34	90.98	22.74	78.72	3.52	99.06	14.98	97.25

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表 3 本文模型在不同条件下的精度实验结果(%)

条件	IITD		MPD		NTU-CP		REST		CASIA		BMPD
条件	EER	Rank-1	EER	Rank-1	EER	Rank-1	EER	Rank-1	EER	Rank-1	EER	Rank-1
定位正常	3.60	99.00	4.97	99.90	1.96	99.90	8.59	90.17	1.21	99.90	6.51	99.88
定位误差	10.15	90.53	6.33	99.58	5.01	96.76	11.76	87.21	2.70	99.43	10.60	99.32
仿射变换	20.59	73.46	8.28	98.21	7.99	90.13	20.67	64.14	5.14	98.33	18.21	92.13

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表 4 不同方法的性能对比

方法	计算量(M)	参数量(M)	GPU运行时间(ms)
本文模型	4927.14	38.44	6.48
CompNet	1053.19	15.04	4.98
CCNet	1688.97	62.52	10.01
CO3Net	2302.40	79.63	10.70
DCPV	2134.62	68.74	9.54
RLANN	2450.40	43.35	7.42
PalmALNet	2030.75	28.62	6.92
MTCC	640.55	4.43	2.84

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表 5 不同尺度消融的EER结果(%)

采用尺度				测试数据集
1.00	1.25	1.50	1.75	IITD	MPD	REST	BMPD
√				5.60	7.67	13.32	11.39
	√			5.87	7.91	13.82	10.55
		√		6.05	8.01	14.37	11.88
√	√			4.81	5.34	10.29	8.74
√		√		5.10	5.73	10.14	7.49
	√	√		4.95	5.38	9.11	8.93
√	√	√		3.60	4.97	8.59	6.51
√	√	√	√	4.26	5.16	8.12	7.84

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表 6 多尺度ROI特征融合机制对不同模型性能的提升结果(%)

方法	IITD		MPD		NTU-CP		REST		CASIA		BMPD
方法	EER	Rank-1	EER	Rank-1	EER	Rank-1	EER	Rank-1	EER	Rank-1	EER	Rank-1
CCNet	↓15.44	↑27.08	↓4.02	↑0.51	↓3.45	↑13.36	↓5.60	↑4.54	↓2.75	↑0.48	↓2.71	↑1.71
CO3Net	↓15.78	↑24.52	↓4.58	↑0.51	↓10.80	↑38.87	↓6.94	↑8.14	↓4.28	↑1.93	↓2.81	↑2.25
RLANN	↓4.34	↑15.48	↓3.00	↑0.41	↓5.61	↑20.02	↓2.34	↑0.66	↓0.40	↑0.30	↓2.30	↑1.45

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表 7 多尺度ROI特征融合机制对性能损耗结果

方法	计算量(M)	参数量(M)	GPU运行时间(ms)
CCNet	↑3443.42	↑6.46	↑2.22
CO3Net	↑4670.28	↑10.46	↑3.50
RLANN	↑5004.04	↑2.91	↑1.05

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