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 implementationsLatest papers
Measuring Hidden Bias within Face Recognition via Racial Phenotypes
We use the set of observable characteristics of an individual face where a race-related facial phenotype is hence specific to the human face and correlated to the racial profile of the subject.
Improvising the Learning of Neural Networks on Hyperspherical Manifold
First, the stereographic projection is implied to transform data from Euclidean space ($\mathbb{R}^{n}$) to hyperspherical manifold ($\mathbb{S}^{n}$) to analyze the performance of angular margin losses.
Facial expression and attributes recognition based on multi-task learning of lightweight neural networks
Moreover, it is shown that the usage of our neural network as a feature extractor of facial regions in video frames and concatenation of several statistical functions (mean, max, etc.)
Facial expression and attributes recognition based on multi-task learning of lightweight neural networks
In this paper, the multi-task learning of lightweight convolutional neural networks is studied for face identification and classification of facial attributes (age, gender, ethnicity) trained on cropped faces without margins.
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.
BroadFace: Looking at Tens of Thousands of People at Once for Face Recognition
Moreover, we propose a novel compensation method to increase the number of referenced instances in the training stage.
Key-Nets: Optical Transformation Convolutional Networks for Privacy Preserving Vision Sensors
Modern cameras are not designed with computer vision or machine learning as the target application.
Deep Polynomial Neural Networks
We introduce three tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks.
GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition
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.
SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action Recognition
Further, we show that our approach generalizes well in experiments on the UTD-MHAD dataset for inertial, skeleton and fused data and the Simitate dataset for motion capturing data.