Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate
This addresses the problem of accurate face recognition under varying poses for security and identification applications, representing an incremental improvement with specific gains.
The paper tackles pose-robust face recognition by proposing a Global Norm-Aware Pooling (GNAP) block that adaptively reweights local features in CNNs, resulting in dramatic reductions in EER and substantial boosts in TPR at low false positive rates, such as at FPR=0.1%.
In this paper, we propose a novel Global Norm-Aware Pooling (GNAP) block, which reweights local features in a convolutional neural network (CNN) adaptively according to their L2 norms and outputs a global feature vector with a global average pooling layer. Our GNAP block is designed to give dynamic weights to local features in different spatial positions without losing spatial symmetry. We use a GNAP block in a face feature embedding CNN to produce discriminative face feature vectors for pose-robust face recognition. The GNAP block is of very cheap computational cost, but it is very powerful for frontal-profile face recognition. Under the CFP frontal-profile protocol, the GNAP block can not only reduce EER dramatically but also boost TPR@FPR=0.1% (TPR i.e. True Positive Rate, FPR i.e. False Positive Rate) substantially. Our experiments show that the GNAP block greatly promotes pose-robust face recognition over the base model especially at low false positive rate.