CompreFace vs Google Cloud Vision API

CompreFace is a free, open-source face recognition service that provides capabilities through a REST API. It supports face recognition and verification, plus face, landmark and mask detection, head pose detection, age recognition and gender recognition. The project says setup and use do not require prior machine-learning skills. It is packaged as a Docker Compose configuration and can run as a standalone server or in the cloud; listed deployment options also include hybrid use. Setup instructions cover Windows, macOS and Linux and require Docker with Docker Compose or Docker Desktop. The service supports CPU and GPU models, with an x86 processor supporting AVX among the stated requirements. A UI panel and role management let users manage roles and control access to face recognition services. The project names FaceNet and InsightFace among its underlying libraries and links JavaScript, Python and .NET SDK repositories. CompreFace is self-hosted and released under Apache 2.0. The documented quick start uses a release archive and starts the service with docker-compose up -d; the project also points users to GitHub channels and a community chat.

Google Cloud Vision API provides image-analysis features that developers can integrate into applications through REST or RPC. Its capabilities include image labeling, face and landmark detection, text recognition, object localization, web detection, and explicit-content tagging. Text Detection reads text in images, while Document Text Detection supports dense text, handwriting, and PDF or TIFF files. Object Localization returns labels and bounding boxes for multiple recognized objects. Web Detection can return related web entities, matching image URLs and pages, visually similar images, and a best-guess label. Face Detection locates faces and facial landmarks and supplies likelihood ratings, but it does not recognize specific individuals. Documented workflows connect it with Cloud Storage, Cloud Functions, Pub/Sub, and Cloud Translation API. For Android and iOS development using Google Cloud Vision services, Google recommends ML Kit for Firebase. Limits include 20 MB per image, 10 MB per JSON request, 16 images per synchronous annotate request, and 2,000 images per asynchronous batch request. The first 1,000 units per feature each month are free; subsequent charges vary by feature and usage tier. The listed plan price is free, with Label Detection listed at 1.50 USD per 1,000 units for 1,001–5,000,000 units.