Hybrid Nature-Inspired Optimization Techniques in Face Recognition

Abstract

AbstractNature has been a very effective source to develop various Nature Inspired Optimisation algorithms and this has developed into an active area of research. The focus of this paper is to develop a Hybrid Nature-inspired Optimisation Technique and study its application in Face Recognition Problem. Two different hybrid algorithms are proposed in this paper. First proposed algorithm is a hybrid of Gravitational Search Algorithm (GSA) and Big Bang-Big Crunch (BBBC). The other algorithm is an improvement of the first algorithm, which incorporates Stochastic Diffusion Search (SDS) algorithm along with Gravitational Search Algorithm (GSA) and Big Bang-Big Crunch (BB-BC). The hybrid is an enhancement of a single algorithm which when incorporated with similar other algorithms performs better in situations where single algorithms fail to perform well. The algorithm is used to optimize the Eigen vectors generated from Principal Component Analysis. The optimized Eigen faces supplied to SVM classifier provides better face recognition capabilities compared to the traditional PCA vectors. Testing on the face recognition problem, the algorithm showed 95% accuracy in the ORL dataset and better optimization capability on functions like Griewank-rosenbrock, Schaffer F7 in comparison to standard algorithms like Rosenbrock, GA and DASA during the Benchmark Testing. KeywordsGravitational Search Algorithm (GSA)Big Bang-Big Crunch (BB-BC)Stochastic Diffusion Search (SDS)Face Recognition ProblemBenchmark Testing