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

Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification