Compreface

1. usage

• Step 1. Install and run CompreFace using our Getting Started guide
• Step 2. sign up for the system and log in.
• Step 3. Create an application (left section) using the “Create” link at the bottom of the page. An application is where you can create and manage your Face Collections.
• Step 4. Enter your application by clicking on its name. Here you will have two options: you can either add new users and manage their access roles or create new Face Services.
• Step 5. To recognize subjects among the known subjects, you need to create Face Recognition Service. After creating a new Face Service, you will see it in the Services List with an appropriate name and API key. After this step, you can look at our demos.
• Step 6. To add known subjects to your Face Collection of Face Recognition Service, you can use REST API. Once you’ve uploaded all known faces, you can test the collection using REST API or the TEST page. We recommend that you use an image size no higher than 5MB, as it could slow down the request process. The supported image formats include JPEG/PNG/JPG/ICO/BMP/GIF/TIF/TIFF.
• Step 7. Upload your photo and let our open-source face recognition system match the image against the Face Collection. If you use a UI for face recognition, you will see the original picture with marks near every face. If you use REST API, you will receive a response in JSON format.
• demos: tutorial_demo.html： 上传人脸数据文件；webcam_demo.html: 调用实时摄像头实现实时人脸识别。

2. component

• Balancer+UI: compreface-fe, runs Nginx, proxies user requests to admin and api servers;
• Admin-server: compreface-admin, responsible for all operations that are done on UI, connects to PostgreSQL database to store the data
• Api servers: compreface-api, handle all user API calls, face recognition, face detection, and face verification, the data synchronization is implemented via postgreSQL notifications.
• Embedding Server: compreface-core, responsible for running networks, which are stateless servers.
• PostgreSQL: password, url, user;

3. custom buids

a trade off between the face recognition accuracy, max throughput of the system and even hardware support.

compreface-core:
    image: ${registry}compreface-core:${CORE_VERSION}
    container_name: "compreface-core"
    ports:
      - "3300:3000"
    runtime: nvidia
    build:
      context: ../embedding-calculator
      args:
        - FACE_DETECTION_PLUGIN=insightface.FaceDetector@retinaface_r50_v1
        - CALCULATION_PLUGIN=insightface.Calculator@arcface_r100_v1
        - EXTRA_PLUGINS=insightface.LandmarksDetector,insightface.GenderDetector,insightface.AgeDetector
        - BASE_IMAGE=${registry}compreface-core-base:base-cuda100-py37
        - GPU_IDX=0
    environment:
      - ML_PORT=3000

4. Face Services

• Face recognition service (Face identification); Face detection service; Face verification service; Age detection plugin; Gender detection plugin; Landmarks detection plugin; Calculator plugin

curl  -X POST "http://localhost:8000/api/v1/recognition/recognize?plugins=age,gender,landmarks" \
-H "Content-Type: multipart/form-data" \
-H "x-api-key: <faces_recognition_api_key>" \
-F file=<local_file>

• Face Recognition Service Endpoints
  • Add a Subject
  • Rename a Subject
  • Delete a Subject
  • Delete All Subjects
  • List Subjects
  • Add an Example of a Subject
  • Recognize Faces from a Given Image
  • List of All Saved Examples of the Subject
  • Delete All Examples of the Subject by Name
  • Delete an Example of the Subject by ID
  • Direct Download an Image example of the Subject by ID
  • Download an Image example of the Subject by ID
  • Verify Faces from a Given Image
• Face Detection Service
• Face Verification Service
• Base64 Support