InsightFace vs MediaPipe
InsightFace
github.comMediaPipe
developers.google.comSide by side
| Figure | InsightFace | MediaPipe |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | Yes |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | Yes | Yes |
| Android | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 3 | 2 |
| Spec score · AI Image Recognition Software | 8.7 (#7) | 8.6 (#8) |
| API extensibility | The server exposes an OpenAPI schema, snake_case REST endpoints and Python methods such as create_collection, add_person and search. | MediaPipe Tasks exposes cross-platform APIs, while the lower-level Framework uses packets, graphs and calculators to build custom pipelines. |
| Company founding | InsightFace says the project was founded in 2018. | Google's official history says Google Inc. was established in August 1998. |
| Privacy | The trust page says operational customer facial images, biometric templates, and inference data are not used to train general-purpose models unless separately agreed in writing. | Solution API inputs such as images, video, and text are processed on-device and are not sent to Google, while usage and performance metrics are sent to Google. |
| Python requirements | The current PyPI package requires Python 3.10 or newer and provides gui, privateframe and face3d extras. | The Python setup supports Windows, macOS, Linux and 64-bit Raspberry OS, requires Python 3.9 or newer and PIP 20.3 or newer. |
| Support | The open-source offering lists community support through GitHub Issues; commercial model licensing lists email support. | Google directs technical questions to Stack Overflow and bug reports or feature requests to GitHub issues, which are limited to community-benefiting problems. |
| What it does | InsightFace is an open-source 2D and 3D face-analysis platform covering recognition, detection, alignment, swapping and related research tools. | MediaPipe supplies customizable machine-learning libraries and tools for deploying AI features in applications across development platforms. |
Plans
InsightFace
- Open SourceFree
- Enterprise & APIPriced on request
- Model LicensingPriced on request
In their own words
InsightFace
InsightFace provides software for face detection, recognition, alignment, and attribute analysis, with optional liveness, person analysis, video face redaction, and face swapping. PersonAnalysis detects people and faces, then matches them to registered identities using face and body references. PrivateFrame supports local video face tracking and blur or mosaic redaction through desktop, command-line, and Python workflows. Integration options include Python, a C/C++ SDK, and a self-hosted REST API. InsightFace Server runs in a Linux container and offers a web interface, REST API, and Python SDK for detection, comparison, enrollment, search, and RTSP monitoring. The Python package uses ONNX Runtime and requires Python 3.10 or newer. The code is MIT licensed, but pretrained models are limited to non-commercial research unless separately licensed for commercial use. The maker presents enterprise applications such as identity verification, access control, mobile and edge authentication, and large-scale face search. Commercial model use requires a separate license.
MediaPipe
MediaPipe gives developers libraries, tools, ready-to-run models and customizable APIs for adding AI and machine learning to applications. Its vision tasks include face and hand landmarks, gesture recognition, image classification and segmentation, object detection, and pose landmarks. Text tasks cover language detection, classification, embeddings, proofreading and summarization, with audio classification also available. MediaPipe Tasks supports Android, Web/JavaScript, Python and iOS; its cross-platform APIs are described as requiring as few as five lines of code for inference. The optimized on-device pipelines use CPU, GPU and TPU acceleration for real-time use cases. Developers can try web samples in MediaPipe Studio with text, webcam input or uploaded images. Model Maker supports transfer learning for compatible models, but it is deprecated and cannot change a model's original task. Solution API inputs are processed on-device and not sent to Google; usage and performance metrics are sent to Google. The framework can be built from source for Linux, macOS, Windows and Docker, with some configurations marked experimental.