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.
