Activity-Biometrics: Person Identification from Daily Activities
Shehreen Azad Yogesh Singh Rawat
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/}