7/25/2019 Facia Recog

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Abstract

The Variations in a face image due to the viewing angle or pose degrades the face

recognition systems considerably. One way of solving this problem is to generate a

virtual frontal view of an image in pose before classification. This has been done by first

estimating the pose of the test face image and then generating its virtual frontal view. The

pose is found by using Artificial Neural Networks (ANN) techniue. The ANN ensemble

used has two layers. The first layer contains feed!forward networks for recogni"ing

predefined poses and the second layer a combinational network. The output of the

network is the pose of the non frontal test image. This information can now be used to

generate a virtual frontal view of the given face.

Two regression techniues #lobal $inear %egression (#$%) and $ocally $inear

%egression ($$%) have been implemented to do this. %egression techniues assume that

an appro&imate linear mapping e&ists between a non!frontal face image and its frontal

counterpart. 'n $$% a non!frontal image is divided into patches. oth non overlapping

patches and overlapped patches have been e&perimented with. The e&perimental results

on #TAV face database shows a comparison between the methods used.

The result rate for #$% was *+ correctly recogni"ed face image for given set of

test images. The result rate for $$% was ,*+ and for $$% overlapping patches was -*+.