Cooperative recognition in Access Control Systems (ACS)
Application
Time and attendance systems and corporate access control systems utilizing biometric terminals or cameras, primarily deployed in well-lit environments. Top priority is ensuring accurate identification without any errors.
Use case requirements
- Frames taken by a camera installed in a room with stable lighting
- One face in the frame, ensuring direct eye contact with the camera
- Image type for detection and identification is "BORDER" (according to NIST), which corresponds to QAA totalscore >= 51%
Recommended configuration files
- access_control_system_one_face_q1.xml
- access_control_system_one_face_q2.xml
- access_control_system_one_face_q3.xml
How to configure
1. Open the ./cfg/image-api.values.yaml file in Image API distribution, find the capturer configuration object (path to the object: processing.services.service name.configs.capturer) and enter the same values for the fields of the capturer object in each detection service: face-detector-face-fitter, face-detector-liveness-estimator, face -detector-template-extractor.
Example:
configs:
capturer:
name: access_control_system_one_face_q2.xml // name of the Face SDK configuration file
2. After editing the file, save it and update Image API in the cluster using the command:
./cli.sh image-api install
Benchmark results
Capturer configuration file | Time to detect one frame (ms) | Detection accuracy (0 to 1) |
access_control_system_one_face_q1.xml | 70 | 0.996 |
access_control_system_one_face_q2.xml | 69 | 0.986 |
access_control_system_one_face_q3.xml | 95 | 0.98 |