TUNED BACTERIAL FORAGING ALGORITHM FOR FACE RECOGNITION TECHNIQUE - Queen Mary, M.Blessy; Singh, N.Nirmal (JOURNAL OF ADVANCES IN CHEMISTRY, 2016)
This article presents an efficient face recognition technique with the optimal selection of components through Bacterial Foraging Algorithm (BFA) based on Support Vector Machines (SVM). The shortcomings in the field of recognition are non-linear and accuracy which has been considered to resolve by an effective classifier. SVMs are kernel machines which uses minimal optimization algorithm for solving non-linear problems and it has a good perspective in face recognition application. This paper also analyzes how the functionality can be improved by choosing optimum parameters. Experimental results reveal that tuned BFA based SVM trained by RBF neural network lends itself to higher face recognition accuracy than normal SVM, BFA and RBF. Therefore the proposed method trained by RBF is of surpassing that of the existing techniques in face recognition. This Chemical journal is preferred because of Bacterial Foraging process of algorithm, viz chemical correlation and bacterium in Image processing.