CompreFace

Overview

CompreFace is a free, open-source service for developers who want to put several face-analysis tasks behind an API on infrastructure they control. It is a strong fit when self-hosting and control over access matter more than avoiding deployment work.

Key features

One REST API covers recognition, verification and detection, plus landmark, mask and head-pose detection, age recognition and gender recognition. That breadth can spare an application from relying on separate services for each task, though teams should choose it only if they can operate the underlying deployment. CompreFace supports custom models and CPU and GPU models, and is based on libraries including FaceNet and InsightFace.

A UI panel and role-management system provide a way to manage roles and control access to face-recognition services. JavaScript, Python and .NET SDK repositories give developers starting points for connecting applications. The project invites contributors to add support for platforms such as Home Assistant and DreamFactory; those are not built-in integrations.

Deployment comes as a Docker Compose configuration. The quick start downloads a release archive and launches the service with docker-compose up -d; it can run on a standalone server or be deployed in the cloud. The project says no prior machine-learning expertise is needed, but that does not remove the operational requirement for Docker and Docker Compose or Docker Desktop. Hardware requirements include an x86 processor with AVX support.

Self-hosting keeps deployment under the user's control, which the project presents as an added data-security guarantee. The software is released under the Apache 2.0 license. Support is community-oriented, with bug and idea reporting on GitHub and a community chat.

Pricing

CompreFace has one plan: CompreFace — free. It is free and open-source, with Docker-based deployment and CPU and GPU support. There are no paid tiers or usage quotas in the plan details provided, so the tradeoff is not a cheaper feature-limited tier; it is taking responsibility for deployment and operation yourself.

Platforms

CompreFace is listed for API, Linux, macOS, web, Windows and self-hosted use. Its setup instructions cover Windows, macOS and Linux, and require Docker plus Docker Compose or Docker Desktop. That makes it flexible across common desktop and server environments, but not a ready-to-use hosted service.

Who it's for

Choose CompreFace if you need multiple face-analysis functions, want API access and role controls, and can run Docker-based software on compatible hardware. It is less suitable for teams seeking a managed service, a mobile-first deployment, or a setup that avoids server administration and hardware prerequisites.

Pros and cons

• Pros: A broad set of face-analysis functions is available through one REST API, reducing the need to assemble separate services.
• Pros: Free, open-source software with self-hosted deployment, CPU and GPU support, and custom models gives teams room to manage their own deployment.
• Pros: A UI and role management address access control, while SDK repositories support JavaScript, Python and .NET integration.
• Cons: Docker and Docker Compose or Docker Desktop are required, and the documented hardware requirements include an x86 processor with AVX support.
• Cons: Teams must manage their own deployment; contributors, rather than built-in integrations, are invited to add support for platforms such as Home Assistant and DreamFactory.

Alternatives

Browse AI Image Recognition Software for more options in the category.

MediaPipe is the better fit when on-device ML solutions and a framework across Android and iOS matter more than CompreFace's self-hosted face-analysis API.

Choose Google Cloud Vision API if you want a pay-as-you-go image API rather than operating a Docker deployment; its usage is billed per image, with each feature applied counted as a billable unit.

LandingLens may suit teams who want a project-oriented vision tool with a free plan that includes 1,000 credits per month, unlimited projects, three users, up to 10,000 images per project and one active project.

Roboflow is worth considering when model training and inference quotas are central: its free tier includes 10 credits a month, described as enough to train about 30 models or run 80,000 inferences.

DeepDetect is another self-hosted option, with an open-source single-machine plan that supports CPU or GPU, but does not include a GPU.

Amazon Rekognition Content Moderation is an alternative for API-based content moderation.

Imagga API is an alternative image API with a free plan that includes 100 requests and basic tagging, categorization, cropping and color solutions.

Nyckel is another API option; its free plan includes 100 invokes per month, up to 200 samples and up to five functions, then charges $0.005 per invoke beyond included usage.

Verdict

CompreFace is a compelling choice for developers and teams that want a free, self-hosted API spanning several face-analysis tasks, with role controls and CPU/GPU support. Pick it for deployment control and breadth; look elsewhere if Docker-based operation or the x86 AVX requirement does not suit your environment.