Neural Network Based Recognizing Surgically Altered Face Images for Real Time Security Application
I E. Sumathi, P. Rajarajeswari, S. Ellammal · 2014
Using a Multi-objective 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 a multiobjective genetic approach that simultaneously optimizes the selection of feature extractor for each face granule along with the weights of individual granules. In this proposed work, using a morphological fusion algorithm with neural network tool is assimilated to match pre and post surgery face images. It contains training and testing datasets. So it gives optimized result. On the plastic surgery face database, the proposed algorithm yields high identification accuracy as compared to existing algorithms and a commercial face recognition system. Our evaluation results obtained using Genetic algorithm with neural network data sets.