Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification
This work addresses the challenge of robust human identification in surveillance applications by dynamically incorporating gait and face cues, though it appears incremental as it builds on existing multimodal fusion methods.
The paper tackled the problem of poor performance of classical fusion techniques in biometric systems under varying conditions by proposing an adaptive multi-biometric fusion strategy using keyless attention deep neural networks, achieving superior performance compared to state-of-the-art models.
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.