OpenFace 3.0: A Lightweight Multitask System for Comprehensive Facial Behavior Analysis
- URL: http://arxiv.org/abs/2506.02891v1
- Date: Tue, 03 Jun 2025 13:56:10 GMT
- Title: OpenFace 3.0: A Lightweight Multitask System for Comprehensive Facial Behavior Analysis
- Authors: Jiewen Hu, Leena Mathur, Paul Pu Liang, Louis-Philippe Morency,
- Abstract summary: OpenFace 3.0 is an open-source toolkit capable of facial landmark detection, facial action unit detection, eye-gaze estimation, and facial emotion recognition.<n>System can be installed and run with a single line of code and operate in real-time without specialized hardware.
- Score: 61.88413918026431
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent years, there has been increasing interest in automatic facial behavior analysis systems from computing communities such as vision, multimodal interaction, robotics, and affective computing. Building upon the widespread utility of prior open-source facial analysis systems, we introduce OpenFace 3.0, an open-source toolkit capable of facial landmark detection, facial action unit detection, eye-gaze estimation, and facial emotion recognition. OpenFace 3.0 contributes a lightweight unified model for facial analysis, trained with a multi-task architecture across diverse populations, head poses, lighting conditions, video resolutions, and facial analysis tasks. By leveraging the benefits of parameter sharing through a unified model and training paradigm, OpenFace 3.0 exhibits improvements in prediction performance, inference speed, and memory efficiency over similar toolkits and rivals state-of-the-art models. OpenFace 3.0 can be installed and run with a single line of code and operate in real-time without specialized hardware. OpenFace 3.0 code for training models and running the system is freely available for research purposes and supports contributions from the community.
Related papers
- FaceInsight: A Multimodal Large Language Model for Face Perception [69.06084304620026]
We propose FaceInsight, a versatile face perception large language model (MLLM) that provides fine-grained facial information.<n>Our approach introduces visual-textual alignment of facial knowledge to model both uncertain dependencies and deterministic relationships among facial information.<n> Comprehensive experiments and analyses across three face perception tasks demonstrate that FaceInsight consistently outperforms nine compared MLLMs.
arXiv Detail & Related papers (2025-04-22T06:31:57Z) - Face-LLaVA: Facial Expression and Attribute Understanding through Instruction Tuning [5.178801281905521]
We propose Face-LLaVA, a large language model for face-centered, in-context learning, including facial expression and attribute recognition.<n>We first developed FaceInstruct-1M, a face-centered database for instruction tuning MLLMs for face processing.<n>We then developed a novel face-specific visual encoder powered by Face-Region Guided Cross-Attention.
arXiv Detail & Related papers (2025-04-09T18:26:07Z) - FaceXFormer: A Unified Transformer for Facial Analysis [59.94066615853198]
FaceXFormer is an end-to-end unified transformer model capable of performing ten facial analysis tasks.<n>Tasks include face parsing, landmark detection, head pose estimation, attribute prediction, age, gender, and race estimation.<n>We train FaceXFormer on ten diverse face perception datasets and evaluate it against both specialized and multi-task models.
arXiv Detail & Related papers (2024-03-19T17:58:04Z) - Faceptor: A Generalist Model for Face Perception [52.8066001012464]
Faceptor is proposed to adopt a well-designed single-encoder dual-decoder architecture.
Layer-Attention into Faceptor enables the model to adaptively select features from optimal layers to perform the desired tasks.
Our training framework can also be applied to auxiliary supervised learning, significantly improving performance in data-sparse tasks such as age estimation and expression recognition.
arXiv Detail & Related papers (2024-03-14T15:42:31Z) - A Generalist FaceX via Learning Unified Facial Representation [77.74407008931486]
FaceX is a novel facial generalist model capable of handling diverse facial tasks simultaneously.
Our versatile FaceX achieves competitive performance compared to elaborate task-specific models on popular facial editing tasks.
arXiv Detail & Related papers (2023-12-31T17:41:48Z) - A Generative Framework for Self-Supervised Facial Representation Learning [18.094262972295702]
Self-supervised representation learning has gained increasing attention for strong generalization ability without relying on paired datasets.
Self-supervised facial representation learning remains unsolved due to the coupling of facial identities, expressions, and external factors like pose and light.
We propose LatentFace, a novel generative framework for self-supervised facial representations.
arXiv Detail & Related papers (2023-09-15T09:34:05Z) - LibreFace: An Open-Source Toolkit for Deep Facial Expression Analysis [7.185007035384591]
We introduce LibreFace, an open-source toolkit for facial expression analysis.
It offers real-time and offline analysis of facial behavior through deep learning models.
Our model also demonstrates competitive performance to state-of-the-art facial expression analysis methods.
arXiv Detail & Related papers (2023-08-18T00:33:29Z) - Towards a Real-Time Facial Analysis System [13.649384403827359]
We present a system-level design of a real-time facial analysis system.
With a collection of deep neural networks for object detection, classification, and regression, the system recognizes age, gender, facial expression, and facial similarity for each person that appears in the camera view.
Results on common off-the-shelf architecture show that the system's accuracy is comparable to the state-of-the-art methods, and the recognition speed satisfies real-time requirements.
arXiv Detail & Related papers (2021-09-21T18:27:15Z) - DeepFaceFlow: In-the-wild Dense 3D Facial Motion Estimation [56.56575063461169]
DeepFaceFlow is a robust, fast, and highly-accurate framework for the estimation of 3D non-rigid facial flow.
Our framework was trained and tested on two very large-scale facial video datasets.
Given registered pairs of images, our framework generates 3D flow maps at 60 fps.
arXiv Detail & Related papers (2020-05-14T23:56:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.