法医网络：用于自动人脸识别的轻量级注意力增强型MobileNetV2
ForensicNet: Lightweight Attention-Enhanced MobileNetV2 for Automated Face Identification
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中文总结 AI 辅助
针对法医环境中自动人脸识别难题，提出法医网络，结合MobileNetV2与CBAM增强注意力，用两阶段迁移学习策略，在公开数据集上表现优于基线架构，准确率高且计算量小，可用于实时法医监控。
中文摘要 AI 辅助
在法医环境中，由于姿势变化、光照变化、遮挡和缺乏标记数据，自动识别犯罪者很困难。本文提出了法医网络，这是一种用于法医面部识别的轻量级深度学习框架，可增强注意力。该模型将MobileNetV2主干与卷积块注意力模块（CBAM）相结合，在保持计算速度的同时，改进判别特征的学习。采用具有自适应层解冻的两阶段迁移学习策略来改善域适应并减少过拟合。使用了LFW和SCFace等公开数据集，有跨越68个身份类别的15000张面部图像。该模型优于AlexNet、ResNet-50和MobileNetV2等基线架构，准确率为92.4%，精确率为90.8%，召回率为89.5%。此外，该框架每次推理仅需2.1 GFLOP，可用于实时法医监控应用。
英文摘要
In forensic environments, automated identification of perpetrators is difficult due to pose changes, changes in light, occlusion, and lack of labeled data. This paper presents ForensicNet, a lightweight deep learning framework for forensic face recognition that enhances attention. The suggested model combines the MobileNetV2 backbone with Convolutional Block Attention Modules (CBAM) to improve the learning of discriminative features while maintaining computational speed. A two-phase transfer learning strategy with adaptive layer unfreezing is used to improve domain adaptation and reduce overfitting. This study used publicly available datasets such as LFW and SCFace, with 15,000 facial images spanning 68 identity classes. The proposed model outperforms baseline architectures such as AlexNet, ResNet-50, and MobileNetV2, with an accuracy of 92.4%, a precision of 90.8%, and a recall of 89.5%. Additionally, the framework requires only 2.1 GFLOPs per inference, and hence can be used in real-time forensic surveillance applications.
