Recognizing surgically altered face images

Abstract

Widespread acceptability and use of biometrics for person authentication has instigated several techniques for evading identification. One such technique is altering facial appearance using surgical procedures that has raised a challenge for face recognition algorithms. Increasing popularity of plastic surgery and its effect on face recognition has attracted attention from the research community. However, the non-linear variations introduced by plastic surgery remain difficult to be modeled by existing face recognition systems. In this research, a multiobjective evolutionary granular algorithm is proposed to match face images before and after plastic surgery. The algorithm first generates non-disjoint face granules at multiple levels of granularity. The granular information is assimilated using an evolutionary genetic algorithm that simultaneously optimizes the selection of feature extractor for each face granule along with the weights of individual granules. The proposed algorithm presents significant improvements in matching surgically altered face images as compared to existing algorithms and a commercial face recognition system.