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
ReFace: Real-time Adversarial Attacks on Face Recognition Systems
We find that the white-box attack success rate of a pure U-Net ATN falls substantially short of gradient-based attacks like PGD on large face recognition datasets.
Fused Deep Neural Network based Transfer Learning in Occluded Face Classification and Person re-Identification
If the matching score was above 90, the recognized label of the person was logged into a file with their name, type of mask, date, and time of recognition.
The Influence of the Other-Race Effect on Susceptibility to Face Morphing Attacks
Similar to humans, the DCNN performed more accurately for original face images than for morphed image pairs.
Towards Privacy-Preserving Neural Architecture Search
Machine learning promotes the continuous development of signal processing in various fields, including network traffic monitoring, EEG classification, face identification, and many more.
Face identification by means of a neural net classifier
This paper describes a novel face identification method that combines the eigenfaces theory with the Neural Nets.
A Deep Dive into Dataset Imbalance and Bias in Face Identification
This is an unfortunate omission, as 'imbalance' is a more complex matter in identification; imbalance may arise in not only the training data, but also the testing data, and furthermore may affect the proportion of identities belonging to each demographic group or the number of images belonging to each identity.
Enhancing Face Recognition With Self-Supervised 3D Reconstruction
In this paper, we propose to enhance face recognition with a bypass of self-supervised 3D reconstruction, which enforces the neural backbone to focus on the identity-related depth and albedo information while neglects the identity-irrelevant pose and illumination information.
Grassmannian learning mutual subspace method for image set recognition
The image set is represented by a low-dimensional input subspace; and this input subspace is matched with reference subspaces by a similarity of their canonical angles, an interpretable and easy to compute metric.
Scale Invariant Domain Generalization Image Recapture Detection
However, most of them ignored the domain generalization scenario and scale variances, with an inferior performance on domain shift situations, and normally were exacerbated by intra-domain and inter-domain scale variances.
A Reliable, Self-Adaptive Face Identification Framework via Lyapunov Optimization
Realtime face identification (FID) from a video feed is highly computation-intensive, and may exhaust computation resources if performed on a device with a limited amount of resources (e. g., a mobile device).
