用热成像人脸图实现自动身份识别，准确率达80%
Automated User Identification from Facial Thermograms with Siamese Networks
- 采用孪生网络自动比对热成像人脸特征
- 在自建数据集上识别准确率约80%
- 适合安全系统研发人员参考
本文研究基于面部热成像的生物识别技术，对比了近红外（NIR）、短波红外（SWIR）、中波红外（MWIR）和长波红外（LWIR）四个波段的性能。论文明确了生物识别系统用热成像仪的关键要求：传感器分辨率、热灵敏度及不低于30 Hz的帧率。提出使用孪生神经网络实现自动化识别，在自有数据集上的实验表明准确率约为80%。同时探讨了可见光与红外光融合系统的潜力，以克服单一模态的局限性。结果表明，热成像技术在构建可靠安全系统方面具有广阔前景。
原文摘要 · Abstract (English)
The article analyzes the use of thermal imaging technologies for biometric identification based on facial thermograms. It presents a comparative analysis of infrared spectral ranges (NIR, SWIR, MWIR, and LWIR). The paper also defines key requirements for thermal cameras used in biometric systems, including sensor resolution, thermal sensitivity, and a frame rate of at least 30 Hz. Siamese neural networks are proposed as an effective approach for automating the identification process. In experiments conducted on a proprietary dataset, the proposed method achieved an accuracy of approximately 80%. The study also examines the potential of hybrid systems that combine visible and infrared spectra to overcome the limitations of individual modalities. The results indicate that thermal imaging is a promising technology for developing reliable security systems.
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