GRAIL-1

Idiap investigate integrated fusion approaches to Deep Neural Network
(DNN)-based whole-body biometrics combining gait and face recognition.
We explore fusion strategies to combine face and whole-body
representations integrating ancillary information from
individual modalities to cope with missing channels and adopt
contrastive training (eg. cross-modal focal loss) to factor the
confidence of other modalities.