Face Identification

34 papers with code • 4 benchmarks • 7 datasets

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.


Use these libraries to find Face Identification models and implementations

Most implemented papers

FaceNet: A Unified Embedding for Face Recognition and Clustering

davidsandberg/facenet CVPR 2015

On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99. 63%.

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

deepinsight/insightface CVPR 2019

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.

SphereFace: Deep Hypersphere Embedding for Face Recognition

wy1iu/sphereface CVPR 2017

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.

CosFace: Large Margin Cosine Loss for Deep Face Recognition

PaddlePaddle/PaddleClas CVPR 2018

The central task of face recognition, including face verification and identification, involves face feature discrimination.

A Light CNN for Deep Face Representation with Noisy Labels

AlfredXiangWu/face_verification_experiment 9 Nov 2015

This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels.

Partial FC: Training 10 Million Identities on a Single Machine

deepinsight/insightface 11 Oct 2020

The experiment demonstrates no loss of accuracy when training with only 10\% randomly sampled classes for the softmax-based loss functions, compared with training with full classes using state-of-the-art models on mainstream benchmarks.

Deep Learning Face Representation from Predicting 10,000 Classes

serengil/deepface 1 Jan 2014

When learned as classifiers to recognize about 10, 000 face identities in the training set and configured to keep reducing the neuron numbers along the feature extraction hierarchy, these deep ConvNets gradually form compact identity-related features in the top layers with only a small number of hidden neurons.

FacePoseNet: Making a Case for Landmark-Free Face Alignment

fengju514/Face-Pose-Net 24 Aug 2017

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.

Editable Neural Networks

editable-ICLR2020/editable ICLR 2020

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

GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition

SeungyounShin/GroupFace CVPR 2020

In the field of face recognition, a model learns to distinguish millions of face images with fewer dimensional embedding features, and such vast information may not be properly encoded in the conventional model with a single branch.