面向步态识别与匿名化的行人属性编辑
Pedestrian Attribute Editing for Gait Recognition and Anonymization
AI总结：
针对步态识别的应用价值与隐私风险双重属性，该研究提出GaitEditor步态属性编辑框架，可通过不同程度属性编辑实现数据增强或去身份化，经多基准验证同时助力步态识别与隐私保护，为首个支持多步态属性编辑的双功能框架。
摘要：
作为一种生物特征识别技术，行人的步态信息因可在远距离、无需目标配合的条件下获取，已受到工业界与学术界的广泛关注。在近年的研究中，该方向既带来了令人振奋的机遇，也引发了值得警惕的挑战：积极层面上，用于嫌疑人检索、安检等安防场景的步态识别应用前景愈发广阔；消极层面上，步态信息的滥用可能引发隐私风险——不法分子即便在目标佩戴口罩、更换衣物的场景下，仍可利用步态特征追踪相关人员。为应对这把“双刃剑”，我们提出了名为GaitEditor（步态编辑器）的步态属性编辑框架。该框架可对真实步态序列执行不同程度的属性编辑，同时保持视觉真实性，分别用于步态数据增强与去身份化，从而根据用户意图自适应地提升或降低步态识别性能。实验方面，我们在三个广泛使用的步态基准数据集上，分别在步态识别与匿名化两种协议下开展了综合评估。大量实验结果表明，GaitEditor的灵活应用可有效提升步态识别性能，同时能生成具备去身份化效果的逼真可视化结果，以保护个人隐私。据我们所知，GaitEditor是首个能够编辑多种步态属性、同时助力步态识别与步态匿名化的框架。GaitEditor的源代码将发布于https://github.com/ShiqiYu/OpenGait。
英文摘要：
As a kind of biometrics, the gait information of pedestrians has attracted widespread attention from both industry and academia since it can be acquired from long distances without the cooperation of targets. In recent literature, this line of research has brought exciting chances along with alarming challenges: On the positive side, gait recognition used for security applications such as suspect retrieval and safety checks is becoming more and more promising. On the negative side, the misuse of gait information may lead to privacy concerns, as lawbreakers can track subjects of interest using gait characteristics even under face-masked and clothes-changed scenarios. To handle this double-edged sword, we propose a gait attribute editing framework termed GaitEditor. It can perform various degrees of attribute edits on real gait sequences while maintaining the visual authenticity, respectively used for gait data augmentation and de-identification, thereby adaptively enhancing or degrading gait recognition performance according to users' intentions. Experimentally, we conduct a comprehensive evaluation under both gait recognition and anonymization protocols on three widely used gait benchmarks. Numerous results illustrate that the adaptable utilization of GaitEditor efficiently improves gait recognition performance and generates vivid visualizations with de-identification to protect human privacy. To the best of our knowledge, GaitEditor is the first framework capable of editing multiple gait attributes while simultaneously benefiting gait recognition and gait anonymization. The source code of GaitEditor will be available at https://github.com/ShiqiYu/OpenGait.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
