Biometric Recognition
The unique feature of the correlator to examine and pick out matches from cluttered or partial data creates an exciting opportunity in the rapidly expanding field of biometric recognition. Full graphical comparisons of captured data are possible in a field where the norm is to look for specific features. But what happens when those features are not captured? The Holy Grail of using standard CCTV footage to pick out the terrorist from a single frame seemingly remains out of reach of current systems.

Fingerprint recognition relies upon identifying enough minutiae points from the sample to guarantee a match. However, many people do not have any minutiae points and cannot therefore be included in normal search process.

In Facial recognition, most processes rely on calculating the distance between the eyes and nose, or on low-resolution eigenfaces. However, these rely on capturing a large proportion of the face and suffer from changes in appearance or expression.

With new 3-D mapping techniques to counter illumination effects and changes in appearance, facial recognition is finally coming of age. CCL is forming collaborative partnerships and projects with leaders in the field of 3-D facial image capture – with particular focus on high-speed detection using high-resolution data and recognition using partially captured images.