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