In this work, we study a novel problem which focuses on person identification while performing daily activities. Learning biometric features from RGB videos is challenging due to spatio-temporal complexity and presence of appearance biases such as clothing color and background. We propose ABNet, a novel framework which leverages disentanglement of biometric and non-biometric features to perform effective person identification from daily activities. ABNet relies on a bias-less teacher to learn biometric features from RGB videos and explicitly disentangle non-biometric features with the help of biometric distortion. In addition, ABNet also exploits activity prior for biometrics which is enabled by joint biometric and activity learning. We perform comprehensive evaluation of the proposed approach across five different datasets which are derived from existing activity recognition benchmarks. Furthermore, we extensively compare ABNet with existing works in person identification and demonstrate its effectiveness for activity-based biometrics across all five datasets. The code and dataset can be accessed at: \url{https://github.com/sacrcv/Activity-Biometrics/}

我们研究了一个关注在进行日常活动时进行人员识别的新问题。由于时空复杂性和服装颜色、背景等外貌偏差的存在，从RGB视频中学习生物特征具有挑战性。我们提出了一个新的框架ABNet，通过对生物特征和非生物特征进行解耦，从而在日常活动中实现有效的人员识别。ABNet依赖于一个无偏的教师，通过RGB视频来学习生物特征，并通过生物特征扭曲显式地解耦非生物特征。此外，ABNet还利用活动先验进行生物识别，这是通过联合生物特征和活动学习实现的。我们在五个不同的数据集上对所提方法进行了全面评估，这些数据集基于现有的活动识别基准。此外，我们还与现有的人员识别方法进行了广泛比较，并证明了ABNet在基于活动的生物识别方面在所有五个数据集上的有效性。代码和数据集可在此处获取：https://github.com/sacrcv/Activity-Biometrics/

活动生物特征：基于日常活动的个人识别