Face

The Face algorithm performs real-time detection, biometric identification and tracking of all detected faces.

Vehicle-Human

The Vehicle-Human (VH) algorithm performs real-time vehicle or human detection, classification and movement tracking.

Automatic license plate recognition

The ALPR algorithm from the SentiVeillance component performs real-time vehicle license plate (number plate) detection and recognition.

Face algorithm

 

Facial identification reliability enables using large watchlist databases (i.e. a watch-list of suspects or a list of company employees). New faces may be enrolled to the database either automatically from video streams or manually as templates, which were created with VeriLook SDK. Persons wearing face masks or respirators can be recognized without separate enrollment.

Face recognition and tracking

Face recognition and tracking is performed in all successive frames from the video source until they disappear from camera's field of view.

Gender classification

For each person in the frame (optional).

Age estimation

For each person in the frame (optional).

Feature detection

Face mask, smile, open-mouth, closed-eyes, glasses, dark-glasses, glasses with a heavy frame, beard and mustache attributes detection (configurable).

Face recognition and tracking

Face recognition and tracking is performed in all successive frames from the video source until they disappear from camera’s field of view.

Gender classification

For each person in the frame (optional).

Age determination

For each person in the frame (optional).

Feature detection

Face mask, smile, open-mouth, closed-eyes, glasses, dark-glasses, glasses with a heavy frame, beard and mustache attributes detection (configurable).

Basic usage recommendations and constrains

Face recognition accuracy of SentiVeillance heavily depends on the quality of a face image in a frame. There are some basic recommendations and constraints when using face recognition applications based on SentiVeillance SDK.

Image quality during enrollment is important, as it influences the quality of the face template. Enrollment from photo or video stream is possible.
Several images during enrollment are recommended for better facial template quality which results in improvement of recognition accuracy and reliability.
Additional enrollments may be needed when facial hair style changes, especially when beard or mustache is grown or shaved off.

1 MegaPixel or better camera resolution is recommended for face enrollment and recognition. Make sure that native resolution is provided by a camera, as some cameras or webcams may scale up native images to higher resolution without image quality improvement.

32 pixels is the recommended minimal distance between eyes for a face on image or video stream to perform face template extraction reliably. 64 pixels or more recommended for better face recognition results. Note that this distance should be native, not achieved by resizing an image.

Face posture tolerance:

  • head roll (tilt) – ±15 degrees;
  • head pitch (nod) – ±15 degrees from frontal position. The head pitch tolerance can be increased up to ±25 degrees if several views of the same face that covered different pitch angles were used during enrollment.
  • head yaw (bobble) – ±90 degrees from frontal position (default value).
    Several views of the same face can be enrolled to the database to cover the whole ±90 degrees yaw range from frontal position.

Technical specifications

4 % of the frame’s larger side (at least 32 pixels) is the minimal recommended distance between eyes for a face on video stream or image to perform reliable face tracking and template extraction. The speeds of face tracking, template extraction and matching against a watchlist database are dependent on actual size of a face in a frame, not on the size of the whole frame.
The performance specifications are provided for Intel Core i7-4771 processor, running at 3.5 GHz clock rate, and 1920 x 1080 pixels videos.

SentiVeillance 8.1 biometric face recognition algorithm technical specifications

Frame rate when tracking up to 5 faces
Face watch-list database matching time*
Single face record size in a template (bytes)
Maximum face watch-list database size
More than 25 frames per second
Less than 0.5 second
194 or 322 (configurable)
Limited by amount of free RAM
* up to 20,000 face records in the database; larger database yelds slower response time. Note that each person may be represented by several records in the database with different appearance variations, different capture angles etc.

Check out other SentiVeillance algorithms