Face verification is the task of comparing a candidate face to another, and verifying whether it is a match. It is a one-to-one mapping: you have to check if this person is the correct one.
( Image credit: Pose-Robust Face Recognition via Deep Residual Equivariant Mapping )
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Face Analysis Project on MXNet
One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large-scale face recognition is the design of appropriate loss functions that enhance discriminative power.
SOTA for Face Identification on MegaFace
The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimize the label noise.
#2 best model for Face Verification on IJB-C
Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images.
#3 best model for Image Retrieval on CARS196
This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space.
#4 best model for Face Identification on MegaFace
The 3D shapes of faces are well known to be discriminative.
#4 best model for 3D Face Reconstruction on Florence (Average 3D Error metric)
This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels.
#2 best model for Age-Invariant Face Recognition on CAFR
Therefore, designing lightweight networks with low memory requirement and computational cost is one of the most practical solutions for face verification on mobile platform.
#2 best model for Face Verification on CFP-FP