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 with no code
LLMs in Political Science: Heralding a New Era of Visual Analysis
We find that Gemini is highly accurate in performing object detection, which is arguably the most common and fundamental task in image analysis for political scientists.
Efficient Verification-Based Face Identification
We study the problem of performing face verification with an efficient neural model $f$.
Weakly Supervised Face and Whole Body Recognition in Turbulent Environments
Face and person recognition have recently achieved remarkable success under challenging scenarios, such as off-pose and cross-spectrum matching.
Open-set Face Recognition using Ensembles trained on Clustered Data
It is composed of clustering and an ensemble of binary learning algorithms that estimates when query face samples belong to the face gallery and then retrieves their correct identity.
Gallery Sampling for Robust and Fast Face Identification
Previous works have been trying to deal with the problem only in training domain, however it can cause much serious problem if the mistakes are in gallery data of face identification.
Watch Out for the Confusing Faces: Detecting Face Swapping with the Probability Distribution of Face Identification Models
We reflect this nature with the confusion of a face identification model and measure the confusion with the maximum value of the output probability distribution.
Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition
In this paper, we aim to address the large domain gap between high-resolution face images, e. g., from professional portrait photography, and low-quality surveillance images, e. g., from security cameras.
Learning Phase Mask for Privacy-Preserving Passive Depth Estimation
With over a billion sold each year, cameras are not only becoming ubiquitous and omnipresent, but are driving progress in a wide range of applications such as augmented/virtual reality, robotics, surveillance, security, autonomous navigation and many others.
Modeling biological face recognition with deep convolutional neural networks
In this review, we summarize the first studies that use DCNNs to model biological face recognition.
Twin identification over viewpoint change: A deep convolutional neural network surpasses humans
These findings also contribute to our understanding of DCNN performance for discriminating high-resemblance faces, demonstrate that the DCNN performs at a level at or above humans, and suggest a degree of parity between the features used by humans and the DCNN.