Face identification is the task of matching a given face image to one in an existing database of faces. It is the second part of face recognition (the first part being detection). It is a one-to-many mapping: you have to find an unknown person in a database to find who that person is.
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On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99. 63%.
#3 best model for Face Verification on Labeled Faces in the Wild
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.
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.
#3 best model for Face Verification on MegaFace
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
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.
SOTA for Face Identification on IJB-B
However, many contemporary face recognition models still perform relatively poor in processing profile faces compared to frontal faces.
SOTA for Face Identification on IJB-A
We motivate and present Ring loss, a simple and elegant feature normalization approach for deep networks designed to augment standard loss functions such as Softmax.
#4 best model for Face Verification on Labeled Faces in the Wild
Conventionally, CNNs have been trained with softmax as supervision signal to penalize the classification loss.
#6 best model for Face Verification on YouTube Faces DB
In this paper, we describe our algorithmic approach, which was used for submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017) group-level emotion recognition sub-challenge.
This analogy allows us to suggest several new optimization algorithms, which exploit the intrinsic "model-selection" capability of the functional to automatically recover the underlying number of clusters.