Face Recognition
556 papers with code • 22 benchmarks • 61 datasets
Facial Recognition is the task of making a positive identification of a face in a photo or video image against a pre-existing database of faces. It begins with detection - distinguishing human faces from other objects in the image - and then works on identification of those detected faces.
The state of the art tables for this task are contained mainly in the consistent parts of the task : the face verification and face identification tasks.
( Image credit: Face Verification )
Benchmarks
These leaderboards are used to track progress in Face Recognition
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Libraries
Use these libraries to find Face Recognition models and implementationsDatasets
LFW
CASIA-WebFace
MS-Celeb-1M
Extended Yale B
MORPH
IJB-B
Adience
VGG Face
RFW
Partial-REID
Subtasks
Most implemented papers
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
DingXiaoH/RepMLP
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We propose RepMLP, a multi-layer-perceptron-style neural network building block for image recognition, which is composed of a series of fully-connected (FC) layers.
CosFace: Large Margin Cosine Loss for Deep Face Recognition
PaddlePaddle/PaddleClas
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CVPR 2018
The central task of face recognition, including face verification and identification, involves face feature discrimination.
Compact Bilinear Pooling
gy20073/compact_bilinear_pooling
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CVPR 2016
Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition.
Deep Face Recognition: A Survey
Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction.
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
The proposed approach achieves an HTER of 0% in Replay Mobile dataset and an ACER of 0. 42% in Protocol-1 of OULU dataset outperforming state of the art methods.
Partial FC: Training 10 Million Identities on a Single Machine
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.
FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
Several conclusions can be drawn from this work, most notably: 1) we managed to employ an existing ICAO compliance framework and a pretrained CNN to automatically label data with quality information, 2) we trained FaceQnet for quality estimation by fine-tuning a pre-trained face recognition network (ResNet-50), and 3) we have shown that the predictions from FaceQnet are highly correlated with the face recognition accuracy of a state-of-the-art commercial system not used during development.
SeesawFaceNets: sparse and robust face verification model for mobile platform
didi/AoE
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arXiv 2019
Therefore, designing lightweight networks with low memory requirement and computational cost is one of the most practical solutions for face verification on mobile platform.
Learning Meta Face Recognition in Unseen Domains
Face recognition systems are usually faced with unseen domains in real-world applications and show unsatisfactory performance due to their poor generalization.
On Complex Valued Convolutional Neural Networks
Doyosae/Deep-Complex-Networks
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The resulting model is shown to be a restricted form of a real valued CNN with twice the parameters.
