Judgment Approved by the court for handing down.
R (Bridges) -v- CC South Wales & ors
Surveillance Camera Code of Practice, reviewing its operation and providing advice
about the Code of Practice. His responsibilities include, in particular, regulating the
use of surveillance cameras and their use in conjunction with AFR technology. In
addition, this Court has received submissions (in writing only) on behalf of a third
intervener, the Police and Crime Commissioner for South Wales.
AFR and its deployment by SWP
7.
An impressive explanation of AFR and its deployment by SWP was given in
considerable detail in the judgment of the Divisional Court. For a full account, reference
should be made to that judgment at [23]-[40]. There has been no criticism of the
accuracy of the Divisional Court’s account. We have, therefore, gratefully taken what
follows from their judgment.
AFR Technology
8.
AFR is a way of assessing whether two facial images depict the same person. A digital
photograph of a person’s face is taken and processed to extract biometric data (i.e.
measurements of the facial features). That data is then compared with facial biometric
data from images contained in a database.
9.
In more detail, the technical operation of AFR comprises the following six stages:
(1) Compiling/using an existing database of images. AFR requires a database of
existing facial images (referred to in this case as “a watchlist”) against which to
compare facial images and the biometrics contained in them. In order for such
images to be used for AFR, they are processed so that the “facial features”
associated with their subjects are extracted and expressed as numerical values.
(2) Facial image acquisition. A CCTV camera takes digital pictures of facial images
in real time. This case is concerned with the situation where a moving image is
captured when a person passes into the camera’s field of view, using a live feed.
(3) Face detection. Once a CCTV camera used in a live context captures footage,
the software (a) detects human faces and then (b) isolates individual faces.
(4) Feature extraction. Taking the faces identified and isolated through “face
detection”, the software automatically extracts unique facial features from the
image of each face, the resulting biometric template being unique to that image.
(5) Face comparison. The AFR software compares the extracted facial features with
those contained in the facial images held on the watchlist.
(6) Matching. When facial features from two images are compared, the AFR
software generates a “similarity score”. This is a numerical value indicating the
likelihood that the faces match, with a higher number indicating a greater
likelihood of a positive match between the two faces. A threshold value is fixed
to determine when the software will indicate that a match has occurred. Fixing
this value too low or too high can, respectively, create risks of a high “false
alarm rate” (i.e. the percentage of incorrect matches identified by the software)
or a high “false reject rate” (i.e. the percentage of true matches that are not in