Creation of Facial Composites from User Selections using Image Gradient

Rubén García-Zurdo

doi:10.31449/inf.v44i1.2340

Abstract

Evolutionary facial composites are created using interactive genetic algorithms based on user selections. This approach is grounded in perceptive studies, and is superior to feature-based systems. A method is presented for creating facial composites in which faces are encoded with shape information, the coordinates of a predefined landmark points, and the image gradient, which represents face information more precisely than image luminance. The new method is accompanied by a Poisson integration process that presents the user with candidate faces. Two user tests, one using composite creators and the other external evaluators, show that the new method produces higher rated composites that are better recognised

Highlights

The goal of facial compositing systems is to create a face image of a target identity from a person's memory so it can be recognised by other people
A within-subject two-way ANOVA was performed for likeness ratings made by constructor participants between Representation (Gradient, Intensity) and Target (DB, George Clooney (GC), Nicolas Cage (NC), Robert De Niro (RN), Tom Cruise (TC), Tom Hanks (TH))
Multiple comparison tests revealed that differences existed between targets David Beckham (DB) and NC [p < .001] and DB and RN [p < .05]. 41.7% of the gradient images received a rating equal to or greater than seven, while only 18.3% of the intensity images received similar ratings

Summary

Introduction

The goal of facial compositing systems is to create a face image of a target identity from a person's memory so it can be recognised by other people. There are two categories of computerised facial composite systems: in feature-based systems, such as E-FIT [1] and PRO-fit [2], the operator selects features as the eyes, nose and mouth and arranges them on a template to create a face from its parts, while in holistic or evolutionary facial compositing, the operator evolves a whole face by 'breeding' selections from an array of face images, via a process of selection by recognition [3] Systems in the latter category include EFIT-V [4], ID [4], INIH [6] and EvoFIT [7]. Humberside police used EvoFIT in 35 criminal investigations, and it led to arrests in 60% of cases [10]

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