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.
Ranked #1 on Face Verification 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.
Ranked #2 on Face Verification on IJB-C
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.
Ranked #4 on Face Identification on MegaFace
Therefore, designing lightweight networks with low memory requirement and computational cost is one of the most practical solutions for face verification on mobile platform.
Ranked #2 on Face Verification on CFP-FP
This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels.
Ranked #2 on Age-Invariant Face Recognition on CAFR
Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.
Ranked #1 on Facial Landmark Detection on 300W (Mean Error Rate metric)