Multi-frame Tilt-angle Face Recognition Using Fusion Re-ranking

Tilt-angle face recognition is a common problem in public video surveillance. Based on the complementarity among multi-frame tilt-angle faces from far to near, it is a possible way to improve recognition performance by fusing them. However, the feature fusion in the existing multi-frame frontal face recognition methods is unsuitable for tilt-angle faces with large changes in resolution and angle. To solve this issue, a multi-frame tilt-angle face recognition approach based on fusion re-ranking is proposed, which can obtain a more accurate final similarity list by weighted fusion of initial similarity lists given by different tilt-angle faces. In order to get the person-specific and angle-specific fusion weights, an angle-guided adaptive weight prediction network is designed. Moreover, we propose a weighted ranking loss to train the network, which can make the gallery face with the same identity as the probe face rank higher in the fused list. Experimental results on the tilt-angle face datasets demonstrate the effectiveness of our method.