Automatic personal identification based on human face profiles

Abstract

This paper proposes a new algorithm of the automatic personal identification using extracted contour lines and feature points from human face profiles. As a decision function for the identification, we use a norm of a 19 dimensional feature vector, the components of which are the weighted distances between two feature points and the angles between two lines connecting three consecutive feature points. The 11 feature points are extracted from a contour line of the input profile expressed by Freeman's chain code using digital curvatures of the line. The effects of deformation of profiles caused by face panning and tilting and mouth opening upon identification accuracy have been investigated in detail. To overcome the deformation effects we propose to register three profiles per person: a normal head position profile, a tilted profile and a panned profile. The simulation results by 68 subjects show that the identification accuracy for the same persons is 91.7% and discrimination accuracy for different persons is 99.9%. This proves the superiority of the proposed algorithm.