Editing Facial recognition system (section)

=== Human identification at a distance (HID) ===
To enable human identification at a distance (HID) low-resolution images of faces are enhanced using [[face hallucination]]. In [[CCTV]] imagery faces are often very small. But because facial recognition algorithms that identify and plot facial features require high resolution images, resolution enhancement techniques have been developed to enable facial recognition systems to work with imagery that has been captured in environments with a high [[signal-to-noise ratio]]. Face hallucination algorithms that are applied to images prior to those images being submitted to the facial recognition system use example-based machine learning with pixel substitution or [[nearest neighbour distribution]] indexes that may also incorporate demographic and age related facial characteristics. Use of face hallucination techniques improves the performance of high resolution facial recognition algorithms and may be used to overcome the inherent limitations of super-resolution algorithms. Face hallucination techniques are also used to pre-treat imagery where faces are disguised. Here the disguise, such as sunglasses, is removed and the face hallucination algorithm is applied to the image. Such face hallucination algorithms need to be trained on similar face images with and without disguise. To fill in the area uncovered by removing the disguise, face hallucination algorithms need to correctly map the entire state of the face, which may be not possible due to the momentary facial expression captured in the low resolution image.<ref>{{Cite book|title=Reliable Face Recognition Methods: System Design, Implementation and Evaluation|author=Harry Wechsler|publisher=Springer Science & Business Media|year=2009|isbn=9780387384641|pages=196}}</ref>