Versions Compared

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

The Face detection basic object is and the Face detection (VL) object are triggered every time a face is captured in the frame. Basic object is enough to perform a search for faces in the archive (see Face search).

In addition, the following types of Face Detection tools detection tools based on metadata from the face detection (see General information on metadata) are available in Axxon Next:

  1. Appearance in area – a detection tool is triggered by the appearance of an object and subsequent face capture in FoVFOV.
  2. Loitering in area –  a detection tool triggered by the lengthy presence of an object  object and its face capture  capture in FoVFOV.
  3. Face recognition evasion detection - triggers when anyone in scene wears dark glasses or covering masks, or uses other masking tricksMask detection – a detection tool is triggered by the face captured with or without a mask.

The Axxon Next database stores all faces in binary form:

  1. All captured facial face images are vectorized* and stored in the t_face_vector table, and their corresponding capture events are stored in the t_json_event table.
  2. Reference images (see Lists of facial templates) are stored in the t_face_listed table.

* Facial Face vector is the mathematical representation of a facial face image created upon face capture.

Info
titleNote

These detection tools require Addon Add-on Face Recognition Pack to be installed (see Installing DetectorPack addons).

...

Note
titleAttention!

With an increase in the number of faces in the database, the statistical error increases: the more faces in the database, the more often similar faces will be recognized when searching in the archive. Accordingly, the degree of similarity when comparing the reference face with the captured face will decrease.

This statistical error is relevant if:

  1. The Camera requirements Requirements for face detection tools are met.
  2. The database contains over a million faces.

An example of the error calculation results of calculating the error:

  1. Face detectordetection, mugshot dataset (good quality photo), 12 million faces in database, and false matching probability is 0.003%. With these initial data, the researchers obtained an identification error of 0.76%.
  2. Face detector detection (VL), the initial data are the same. The identification error is 0.81%.

...