Face Identification

41 papers with code • 4 benchmarks • 5 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.

Libraries

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

Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability.

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.

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.

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.

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.

DeepID3: Face Recognition with Very Deep Neural Networks

serengil/deepface 3 Feb 2015

Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity.

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

xtinkt/editable ICLR 2020

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

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.