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
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Libraries
Use these libraries to find Face Detection models and implementations
ADE20K
Manga109
FDDB
AFW
UMDFaces
PASCAL Face
COCO-WholeBody
WIDER
MALF
Latest papers with no code
ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning
The rapid development of diffusion models has triggered diverse applications.
Efficient optimal dispersed Haar-like filters for face detection
This paper introduces a new dispersed Haar-like filter for efficiently detection face.
PIE: Physics-inspired Low-light Enhancement
In this paper, we propose a physics-inspired contrastive learning paradigm for low-light enhancement, called PIE.
VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis
We propose VLOGGER, a method for audio-driven human video generation from a single input image of a person, which builds on the success of recent generative diffusion models.
Face Swap via Diffusion Model
This technical report presents a diffusion model based framework for face swapping between two portrait images.
SecurePose: Automated Face Blurring and Human Movement Kinematics Extraction from Videos Recorded in Clinical Settings
The extraction of quantifiable kinematic features can help inform movement disorder assessment in these videos, but existing methods to do this are prone to errors if using pre-blurred videos.
Face Detection: Present State and Research Directions
The majority of computer vision applications that handle images featuring humans use face detection as a core component.
Look, Listen and Recognise: Character-Aware Audio-Visual Subtitling
The goal of this paper is automatic character-aware subtitle generation.
Low-power event-based face detection with asynchronous neuromorphic hardware
We show that the power consumption of the chip is directly proportional to the number of synaptic operations in the spiking neural network, and we explore the trade-off between power consumption and detection precision with different firing rate regularization, achieving an on-chip face detection mAP[0. 5] of ~0. 6 while consuming only ~20 mW.
Enhancing Vehicle Entrance and Parking Management: Deep Learning Solutions for Efficiency and Security
To solve the problem of auto management of vehicle entrance and parking, we have utilized state-of-the-art deep learning models and automated the process of vehicle entrance and parking into any organization.
