MobileFace

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MobileFace is an open-source face recognition solution designed for mobile devices. It offers real-time face detection, identification, and various facial analysis capabilities, emphasizing efficient performance on edge devices.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is MobileFace?

MobileFace is an open-source project providing a comprehensive face recognition solution optimized for mobile devices. It encompasses a range of functionalities including real-time face detection, identification, landmark detection, pose estimation, alignment, and attribute analysis. The solution leverages receptive field awareness for natural anchor point detection, contributing to its fast performance. It supports various models like MobileFace_Identification_V3, which boasts impressive speed and accuracy. The project provides examples for integrating its features using MXNet and GluonCV, making it accessible for developers looking to implement advanced facial processing on mobile platforms. Additionally, it includes tools for visualization, performance benchmarking, and model pruning.

Best used for

Ideal for developers who need to integrate real-time face recognition into mobile applications, data scientists looking to benchmark and optimize facial AI models, and researchers exploring efficient on-device AI solutions. Especially valuable for projects requiring high-performance facial processing on resource-constrained mobile platforms.

Common actions

implement face recognition

develop mobile AI

optimize AI models

analyze facial features

deepfakeautomated workflowworkflowslow-code/no-codecollaboration"AI Agents"face swappinggithub copilotopen-source

Capabilities

Key features

Real-time face detection
Face identification
Landmark detection
Pose estimation
Face alignment
Attribute analysis
Model pruning

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What frameworks does MobileFace support for its models?

MobileFace primarily supports MXNet and GluonCV for its identification, detection, and attribute models. It also utilizes DLib for landmark detection. The project provides examples and tools for working with these frameworks to integrate facial processing capabilities.

Can MobileFace models be optimized for smaller sizes?

Yes, MobileFace includes tools for model pruning. This allows developers to delete needless layers from MXNet models, such as classify and loss layers, retaining only feature layers to decrease the model size specifically for inference on mobile devices.

How fast is MobileFace's identification model on mobile devices?

The MobileFace_Identification_V3 model is highly optimized, achieving a performance of approximately 3ms on CPU, making it a state-of-the-art solution for real-time face identification on mobile devices. Other models also offer fast inference times.

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