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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Our approach based on a signal-level formulation remains flexible across a variety of modalities while outperforming the baseline on the large scale NTU RGB+D 120 dataset for the One-Shot action recognition protocol by 4. 2%.
Ranked #1 on One-Shot 3D Action Recognition on NTU RGB+D 120
The usage of convolutional neural networks (CNNs) in conjunction with a margin-based softmax approach demonstrates a state-of-the-art performance for the face recognition problem.
Combined variations containing low-resolution and occlusion often present in face images in the wild, e. g., under the scenario of video surveillance.
To improve the discriminative and generalization ability of lightweight network for face recognition, we propose an efficient variable group convolutional network called VarGFaceNet.
Ranked #1 on Face Verification on CFP-FP
However, many contemporary face recognition models still perform relatively poor in processing profile faces compared to frontal faces.
Ranked #1 on 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.
Ranked #10 on Face Verification on Labeled Faces in the Wild
More specifically, we reformulate the softmax loss as a cosine loss by $L_2$ normalizing both features and weight vectors to remove radial variations, based on which a cosine margin term is introduced to further maximize the decision margin in the angular space.
Ranked #3 on Face Identification on MegaFace
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 Identification on MegaFace