Non-cooperative recognition in Access Control Systems (ACS)

Application

Non-corporate access control systems (such as facial recognition payment in transportation and visitor tracking in gyms) implemented using cameras without the need for specialized biometric terminals. Our main focus is on fast and accurate detection and identification, ensuring smooth and efficient entry without queues or delays. Top priority is to eliminate identification errors and provide a seamless experience for all users.

Use case requirements

• Face detection from frames taken within a crowd, even from side angles
• Frames captured in an indoor environment with consistent lighting
• Up to 5-8 faces in the frame. Only the face closest to the camera is identified
• Image type for detection and identification is "WILD" (according to NIST), which corresponds to QAA totalscore >= 40%

Recommended configuration files

• access_control_system_several_faces_q1.xml
• access_control_system_several_faces_q2.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_several_faces_q1.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_several_faces_q1.xml	969	0.946
access_control_system_several_faces_q2.xml	95	0.936