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.
Benchmarks
These leaderboards are used to track progress in Face Identification
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Libraries
Use these libraries to find Face Identification models and implementations
MS-Celeb-1M
MegaFace
IJB-A
IJB-B
LTFT
Latest papers with no code
Face Identification Proficiency Test Designed Using Item Response Theory
Multiple tests of equal difficulty can be constructed then using subsets of items.
Demographic Fairness in Face Identification: The Watchlist Imbalance Effect
Recently, different researchers have found that the gallery composition of a face database can induce performance differentials to facial identification systems in which a probe image is compared against up to all stored reference images to reach a biometric decision.
Face Attributes as Cues for Deep Face Recognition Understanding
Deeply learned representations are the state-of-the-art descriptors for face recognition methods.
Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study
Based on this approach, we build FaceBehaviorNet, the first framework for large-scale face analysis, by jointly learning all facial behavior tasks.
Face-GCN: A Graph Convolutional Network for 3D Dynamic Face Identification/Recognition
This has two disadvantages.
Towards On-Device Face Recognition in Body-worn Cameras
Face recognition technology in body-worn cameras is used for surveillance, situational awareness, and keeping the officer safe.
ClusterFace: Joint Clustering and Classification for Set-Based Face Recognition
Deep learning technology has enabled successful modeling of complex facial features when high quality images are available.
Reconstructing A Large Scale 3D Face Dataset for Deep 3D Face Identification
The experimental results show that the reconstructed 3D facial surfaces are useful and our 2D-aided deep 3D face identification framework is meaningful, facing the scarcity of 3D faces.
Taking Modality-free Human Identification as Zero-shot Learning
There have been numerous methods proposed for human identification, such as face identification, person re-identification, and gait identification.
BWCFace: Open-set Face Recognition using Body-worn Camera
To this aim, the contribution of this work is two-fold: (1) collection of a dataset called BWCFace consisting of a total of 178K facial images of 132 subjects captured using the body-worn camera in in-door and daylight conditions, and (2) open-set evaluation of the latest deep-learning-based Convolutional Neural Network (CNN) architectures combined with five different loss functions for face identification, on the collected dataset.
