  Biometrics plays a significant role in vision-based surveillance
applications. Soft biometrics such as gait is widely used with face in
surveillance tasks like person recognition and re-identification. Nevertheless,
in practical scenarios, classical fusion techniques respond poorly to changes
in individual users and in the external environment. To this end, we propose a
novel adaptive multi-biometric fusion strategy for the dynamic incorporation of
gait and face biometric cues by leveraging keyless attention deep neural
networks. Various external factors such as viewpoint and distance to the
camera, are investigated in this study. Extensive experiments have shown
superior performanceof the proposed model compared with the state-of-the-art
model.
