Face Detection
133 papers with code • 13 benchmarks • 36 datasets
Face Detection is a computer vision task that involves automatically identifying and locating human faces within digital images or videos. It is a fundamental technology that underpins many applications such as face recognition, face tracking, and facial analysis.
( Image credit: insightface )
Benchmarks
These leaderboards are used to track progress in Face Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Face Detection models and implementations
ADE20K
Manga109
FDDB
AFW
UMDFaces
PASCAL Face
COCO-WholeBody
WIDER
MALF
Latest papers
Filter-Pruning of Lightweight Face Detectors Using a Geometric Median Criterion
idt-iti/lightweight-face-detector-pruning
•
•
Face detectors are becoming a crucial component of many applications, including surveillance, that often have to run on edge devices with limited processing power and memory.
Appearance-based gaze estimation enhanced with synthetic images using deep neural networks
Human eye gaze estimation is an important cognitive ingredient for successful human-robot interaction, enabling the robot to read and predict human behavior.
Zero-Shot Enhancement of Low-Light Image Based on Retinex Decomposition
Two difficulties here make low-light image enhancement a challenging task; firstly, it needs to consider not only luminance restoration but also image contrast, image denoising and color distortion issues simultaneously.
The Fairness Stitch: Unveiling the Potential of Model Stitching in Neural Network De-Biasing
modar7/the_fairness_stitch
•
•
The pursuit of fairness in machine learning models has emerged as a critical research challenge in different applications ranging from bank loan approval to face detection.
How Robust is Google's Bard to Adversarial Image Attacks?
By attacking white-box surrogate vision encoders or MLLMs, the generated adversarial examples can mislead Bard to output wrong image descriptions with a 22% success rate based solely on the transferability.
Low-Light Image Enhancement with Wavelet-based Diffusion Models
JianghaiSCU/Diffusion-Low-Light
•
•
Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration.
Detecting Adversarial Faces Using Only Real Face Self-Perturbations
cc13qq/sapd
•
•
Adversarial attacks aim to disturb the functionality of a target system by adding specific noise to the input samples, bringing potential threats to security and robustness when applied to facial recognition systems.
Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video
wenzhengzeng/mpeblink
•
•
CVPR 2023
Experiments on MPEblink verify the essential challenges of real-time multi-person eyeblink detection in the wild for untrimmed video.
FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model
vvictoryuki/freedom
•
•
ICCV 2023
In this work, we propose a training-Free conditional Diffusion Model (FreeDoM) used for various conditions.
Image Enhancement for Remote Photoplethysmography in a Low-Light Environment
Using collected dataset, we found 1) face detection algorithm cannot detect faces in video captured in low light conditions; 2) A decrease in the amplitude of the pulsatile signal will lead to the noise signal to be in the dominant position; and 3) the chrominance-based method suffers from the limitation in the assumption about skin-tone will not hold, and Green and ICA method receive less influence than POS in dark illuminance environment.
