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