Face Recognition Value Proposition

50 ways to use face recognition

CASINO I-CUBE Face Recognition Solution (PDF - 1.5 MB)

I-Cube Face Recognition System design and costing

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I-Cube Advantage

The Underlying Algorithm

Fundamental to any face recognition system is the way in which faces are coded. FaceItŪ uses Local Feature Analysis (LFA) to represent facial images in terms of local statistically derived building blocks.

LFA is a mathematical technique developed by Dr. Joseph Atick and his colleagues at Rockefeller University. LFA is based on the realization thatall facial images (for that matter all complex patterns) can be synthesized from an irreducible set of building elements.

These elements are derived from a representative ensemble of faces using sophisticated statistical techniques. They span multiple pixels (but are still local) and represent universal facial shapes, but are not exactly the commonly known facial features.

In fact, there are many more facial building elements than there are facial parts. However, it turns out that synthesizing a given facial image, to a high degree of precision, requires only a small subset (12-40 characteristic elements) of the total available set. Identity is determined not only by which elements are characteristic, but also by the manner in which they are geometrically combined (i.e. their relative positions).

Using LFA, FaceItŪ maps an individual's identity into a complex mathematical formula - called a faceprint - that can be matched and compared to others with phenomenal accuracy. It is resistant to changes in lighting, skin tone, eyeglasses, facial expression and hair and is robust with respect to pose variations, up to 35 degrees in all directions.

The faceprint contains the information that distinguishes a face from millions of others. And our latest advances allow the FaceItŪ engine to scan through these millions of records in the blink of an eye.

I-Cube.   All rights reserved.  Revised: January 03, 2008 .