Personal devices have adopted diverse authentication methods, including biometric recognition and passcodes. In contrast, headphones have limited input mechanisms, depending solely on the authentication of connected devices. We present Moonwalk, a novel method for passive user recognition utilizing the built-in headphone accelerometer. Our approach centers on gait recognition; enabling users to establish their identity simply by walking for a brief interval, despite the sensor's placement away from the feet. We employ self-supervised metric learning to train a model that yields a highly discriminative representation of a user's 3D acceleration, with no retraining required. We tested our method in a study involving 50 participants, achieving an average F1 score of 92.9% and equal error rate of 2.3%. We extend our evaluation by assessing performance under various conditions (e.g. shoe types and surfaces). We discuss the opportunities and challenges these variations introduce and propose new directions for advancing passive authentication for wearable devices.

翻译：个人设备已采用多种身份认证方法，包括生物特征识别和密码。相比之下，耳机因其输入机制有限，完全依赖已连接设备的认证。本文提出Moonwalk——一种利用内置耳机加速度传感器实现被动用户识别的新型方法。该方法以步态识别为核心，尽管传感器远离足部，用户仅需短时间行走即可完成身份验证。我们采用自监督度量学习训练模型，无需重新训练即可输出用户三维加速度的高度判别性表征。在包含50名受试者的实验中，该方法实现了平均F1分数92.9%、等错误率2.3%的性能。我们进一步评估了不同条件（如鞋类类型和地面材质）下的表现，探讨了这些变量带来的机遇与挑战，并为推动可穿戴设备的被动身份认证提出了新方向。