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Deep Convolutional Neural Network Using Triplets of Faces, Deep Ensemble, and Score-Level Fusion for Face Recognition
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
This paper proposes a new face verification method that uses multiple deep convolutional neural networks (DCNNs) and a deep ensemble, that extracts two types of low dimensional but discriminative and high-level abstracted features from each DCNN, then combines them as a descriptor for face verification. Our DCNNs are built from stacked multiscale convolutional layer blocks to present multi-scale abstraction. To train our DCNNs, we use different resolutions of triplets that consist of reference
doi:10.1109/cvprw.2017.89
dblp:conf/cvpr/KangKK17
fatcat:4nuq242yrndjxgdmxbnqsbaowi