Face Identification

17 papers with code · Computer Vision

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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Latest papers with code

Editable Neural Networks

ICLR 2020 editable-ICLR2020/editable

We empirically demonstrate the effectiveness of this method on large-scale image classification and machine translation tasks.

FACE IDENTIFICATION IMAGE CLASSIFICATION MACHINE TRANSLATION SELF-DRIVING CARS

15
01 Jan 2020

FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task Learning

4 Nov 2019swordcheng/FCSR-GAN

Combined variations containing low-resolution and occlusion often present in face images in the wild, e. g., under the scenario of video surveillance.

FACE IDENTIFICATION FACIAL INPAINTING MULTI-TASK LEARNING SUPER-RESOLUTION

28
04 Nov 2019

VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition

11 Oct 2019zma-c-137/VarGFaceNet

To improve the discriminative and generalization ability of lightweight network for face recognition, we propose an efficient variable group convolutional network called VarGFaceNet.

FACE DETECTION FACE IDENTIFICATION FACE RECOGNITION

213
11 Oct 2019

Git Loss for Deep Face Recognition

23 Jul 2018kjanjua26/Git-Loss-For-Deep-Face-Recognition

Conventionally, CNNs have been trained with softmax as supervision signal to penalize the classification loss.

FACE IDENTIFICATION FACE RECOGNITION FACE VERIFICATION

28
23 Jul 2018

Pose-Robust Face Recognition via Deep Residual Equivariant Mapping

CVPR 2018 penincillin/DREAM

However, many contemporary face recognition models still perform relatively poor in processing profile faces compared to frontal faces.

FACE IDENTIFICATION FACE RECOGNITION FACE VERIFICATION ROBUST FACE RECOGNITION

327
02 Mar 2018

Ring loss: Convex Feature Normalization for Face Recognition

CVPR 2018 Paralysis/ringloss

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.

FACE IDENTIFICATION FACE RECOGNITION FACE VERIFICATION

77
28 Feb 2018

CosFace: Large Margin Cosine Loss for Deep Face Recognition

CVPR 2018 cvqluu/Angular-Penalty-Softmax-Losses-Pytorch

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.

FACE IDENTIFICATION FACE RECOGNITION FACE VERIFICATION

108
29 Jan 2018

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

CVPR 2019 deepinsight/insightface

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.

FACE IDENTIFICATION FACE RECOGNITION FACE VERIFICATION

6,135
23 Jan 2018

Group-level Emotion Recognition using Transfer Learning from Face Identification

6 Sep 2017arassadin/emotiw2017

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.

EMOTION RECOGNITION FACE IDENTIFICATION TRANSFER LEARNING

18
06 Sep 2017

FacePoseNet: Making a Case for Landmark-Free Face Alignment

24 Aug 2017fengju514/Face-Pose-Net

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.

FACE ALIGNMENT FACE IDENTIFICATION FACE RECOGNITION FACE VERIFICATION FACIAL LANDMARK DETECTION

422
24 Aug 2017