摘要：

在当今社会, 指纹识别技术得到广泛使用并占据个人身份认证的大部分市场。性别是区分人与人的最基本的特征之一, 性别分类对于调查刑事犯罪和性别冒充至关重要。目前, 已有许多利用指纹脊数等物理特征进行指纹性别识别的方法, 但基于传统手工特征的识别方法难以应用在复杂多变的场景中。为此, 提出一种基于多尺度注意力机制和多模型融合策略的指纹性别识别方法FGRNet。首先, 在稠密块中引入深度可分离卷积与CBAM(Convolutional Block Attention Module)注意力机制, 在不增加参数量的同时提高网络深度与广度; 其次, 在CBAM模块中引入多尺度结构, 以较低的模型复杂度学习注意力权重, 并有效地整合局部注意力和全局注意力, 从而建立远程通道依赖, 使得网络提取的特征更具判别性; 最后, 利用不同模型之间的互补性, 设计基于证据理论的多模型融合策略, 进一步提升识别精度。实验结果表明, 在公开数据集SOCOFing和自建数据集上, FGRNet的准确率分别达到82.655 8%和91.149 0%, 且模型具有良好的鲁棒性, 在指纹图像包含大量无关噪声的情况下仍能达到较好的识别效果。

关键词: 性别识别, 指纹, 多模型融合, 多尺度注意力机制, D-S证据理论

Abstract:

Today, fingerprint recognition technology is widely used and holds the largest market share in personal identity authentication. Gender is one of the most fundamental characteristics that distinguishes individuals, and gender classification is crucial for investigating criminal offenses and gender impersonation. Currently, many fingerprint gender recognition methods use physical features such as fingerprint ridges; however, applying traditional, manual feature-based recognition methods in complex and changing scenarios is difficult. To address this issue, this paper proposes a fingerprint gender recognition method, FGRNet, which is based on the multi-scale attention mechanism and a multi-model fusion strategy. First, introducing depthwise separable convolution and the Convolutional Block Attention Module (CBAM) attention mechanism in dense blocks improves the depth and breadth of the network without increasing the number of parameters. Second, a multi-scale structure is introduced in CBAM to learn attention weights with lower model complexity and effectively integrate local and global attention, thereby establishing remote channel dependencies and enabling the network to extract features that are more discriminative. Finally, utilizing the complementarity between different models, a multi-model fusion strategy based on evidence theory is designed to further improve the recognition accuracy. Experimental results show that FGRNet achieves accuracies of 82.655 8% and 91.149 0% on the public dataset SOCOFing and a self-built dataset, respectively. The proposed model is robust and achieves good recognition performance even on fingerprint images containing a large amount of irrelevant noise.

Key words: gender recognition, fingerprint, multi-modal fusion, multi-scale attention mechanisms, D-S evidential theory