摘 要
随着智能安防需求的不断增长，人脸识别技术作为一项关键的人工智能应用，在提升公共安全和智能化管理方面具有重要意义本研究以智能安防中的人脸识别技术为核心，旨在解决复杂场景下人脸检测与识别的准确性和实时性问题通过分析现有算法的优缺点，提出了一种基于深度学习的改进人脸识别框架该框架结合轻量化卷积神经网络与注意力机制，优化了模型在低分辨率和遮挡条件下的表现同时，引入自适应特征增强模块，进一步提升了对光照变化和姿态差异的鲁棒性实验采用公开数据集及实际场景采集的数据进行验证，结果表明所提方法在识别精度和运行效率上均优于传统算法此外，为满足实际部署需求，设计并实现了嵌入式人脸识别系统，支持多设备协同工作和云端数据管理研究结论显示，改进的算法框架不仅显著提高了人脸识别性能，还具备良好的可扩展性和适应性，为智能安防领域的技术升级提供了新的思路和解决方案
关键词：人脸识别 深度学习 智能安防 注意力机制
Abstract
With the continuous growth in demand for intelligent security, facial recognition technology, as a key application of artificial intelligence, plays a significant role in enhancing public safety and intelligent management. This study focuses on facial recognition technology in intelligent security systems, aiming to address the challenges of accuracy and real-time performance in complex scenarios. By analyzing the advantages and limitations of existing algorithms, an improved facial recognition fr amework based on deep learning is proposed. This fr amework integrates lightweight convolutional neural networks with attention mechanisms, optimizing model performance under low-resolution and occlusion conditions. Additionally, an adaptive feature enhancement module is introduced to further improve robustness against variations in lighting and pose. Experiments conducted using publicly available datasets and data collected from real-world scenarios demonstrate that the proposed method outperforms traditional algorithms in terms of recognition accuracy and operational efficiency. To meet practical deployment requirements, an embedded facial recognition system has been designed and implemented, supporting multi-device collaboration and cloud-based data management. The conclusions indicate that the improved algorithmic fr amework not only significantly enhances facial recognition performance but also exhibits excellent scalability and adaptability, providing new insights and solutions for technological upgrades in the field of intelligent security.
Keyword:Face Recognition Deep Learning Intelligent Security Attention Mechanism
目 录
1绪论 1
1.1智能安防中人脸识别技术的背景 1
1.2人脸识别技术在智能安防中的意义 1
1.3国内外研究现状与发展趋势 1
1.4本文研究方法与技术路线 2
2面向智能安防的人脸识别关键技术分析 2
2.1人脸识别的基本原理与流程 2
2.2特征提取算法在安防中的应用 3
2.3数据预处理与质量优化技术 3
2.4跨场景人脸识别挑战与应对策略 4
2.5实时性与准确性的平衡研究 4
3智能安防中人脸识别系统的实现方案 4
3.1系统架构设计与功能模块划分 4
3.2数据采集与标注技术研究 5
3.3基于深度学习的模型训练方法 5
3.4系统性能评估指标体系构建 6
3.5安防场景下的系统部署与优化 6
4面向智能安防的人脸识别技术应用与验证 7
4.1典型安防场景需求分析 7
4.2技术应用案例研究与效果评估 7
4.3安全性与隐私保护问题探讨 8
4.4系统鲁棒性测试与改进措施 8
4.5未来发展方向与潜在突破点 9
结论 9
参考文献 11
致谢 12
