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.

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.

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.

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.

GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations

HamadYA/GhostFaceNets IEEE Access 2023

The development of deep learning-based biometric models that can be deployed on devices with constrained memory and computational resources has proven to be a significant challenge.