From: believer@telepath.com Subject: IP: Machine Vision For Biometric Applications Date: Wed, 09 Sep 1998 09:07:57 -0500 To: believer@telepath.com ---------------------- NOTE: Document contains instructive images. You should go to the website and open the images. ---------------------- Source: Applied Optics Group at the University of Kent at Canterbury (U.K.) http://speke.ukc.ac.uk/physical-sciences/aog/facereco/ Machine Vision For Biometric Applications We are currently conducting research in the areas of automated facial recognition and data compression of digitised images of the human face. This work began by performing an eigenfactor analysis on a data set comprising 290 faces drawn largely from the student population at the University of Kent, Canterbury. The image below shows the first three components (eigenfaces) resulting from this analysis. It is interesting to note that eigenfaces 2 and 3 have a clear relationship to gender. Thus the addition of eigenface 2 to the average eigenface (1) results in a feminine face whereas the subtraction of face 2 produces a face having masculine characteristics. In a similar way, addition of face 3 to the average produces a masculine face and subtraction of face 3 from the average results in a face having feminine features. This approach, variously known as the Karhunen-Loeve expansion, eigenfactor analysis, principal components or the Hotelling transform has exceptional data compression properties when applied to this particular pattern class (2-D images of human faces). Below, we show the image reconstruction quality that is achievable using codes of varying lengths which describe how to reconstitute the image using the component eigenfaces as a basis. Note that the subject shown here was not included in the original data set used to generate the eigenface basis. Despite this, recognition is achieved using a very short code. This method works particularly well when conditions such as head-camera orientation and subject illumination are controlled.We are now investigating other methods (some related to the Karhunen-Loeve expansion, some not) which may be suitable for automated facial recognition under less benign conditions. In particular, we are beginning to investigate the use of illumination compensation techniques and 3-D imaging techniques which are independent of illumination conditions. The group working on facial recognition here at UKC collaborates with a number of commercial/industrial organisations in the U.K. and Europe. We currently await the outcome of a cooperative research bid (CRAFT) to the European Commission which will involve the development of facial biometrics for smart cards and other access control applications. The industrial partners are Neural Computer Sciences (U.K.), Datastripe Ltd (U.K.), Inside Technologies (France), Smartkort (Iceland) and A la Carte (Belgium). More links on Facial Recognition Staff Involved Dr. C.J. Solomon - E-mail: C.J.Solomon@ukc.ac.uk Jamie P. Brooker - E-mail: jpb3@ukc.ac.uk ----------------------- NOTE: In accordance with Title 17 U.S.C. section 107, this material is distributed without profit or payment to those who have expressed a prior interest in receiving this information for non-profit research and educational purposes only. For more information go to: http://www.law.cornell.edu/uscode/17/107.shtml ----------------------- ********************************************** To subscribe or unsubscribe, email: majordomo@majordomo.pobox.com with the message: (un)subscribe ignition-point email@address ********************************************** www.telepath.com/believer **********************************************