Enhanced Face Recognition by Fusion of Global and Local Features under Varying Illumination

ICITCS（2014）

引用 8|浏览5

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摘要

We propose a new method to enhance performance of face recognition under varying lightings by applying score-level fusion between global and local Fourier-Mellin Transform (FMT) features based SVM. An optimal method based Particle Swarm Optimization (PSO) is used to find optimal weights to fuse the aforementioned information at score-level. The results on Korean face database demonstrate that our proposed method outperforms standard global feature, local feature and other well-known methods. Specifically, the best recognition rate is, respectively, 89% and 85% for indoor and outdoor images.

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关键词

fourier transforms,face recognition,particle swarm optimisation,support vector machines,visual databases,fmt features,fourier-mellin transform,korean face database,pso,face recognition enhancement,global features,indoor images,local features,optimal weights,outdoor images,particle swarm optimization,score-level fusion,varying illumination,varying lightings,local binary pattern

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