Reducing the Computational Cost of SVM in Face Recognition Application Using Hybrid Cultural Algorithm
Journal Title: IOSR Journal of Dental and Medical Sciences (IOSR-JDMS) - Year 2018, Vol 17, Issue 4
Abstract
The computational cost in terms of computation time and memory utilization of various techniques in the development of face recognition applications is a major challenge. The fundamental properties of high dimension exhibited by face images leads to superfluous information that instigates computational burden in term of processing speed and memory usage.Support Vector Machine despite being a wonderful face recognition technique due its generalization capability and high theoretical background of classification accuracy consumes large amount of time and memory: a major setback in its implementation in real world applications. Consequently, a hybrid cultural algorithm is proposed to reduce the computational cost of SVM in face recognition applications. Cultural Algorithm optimises the parameter of SVM to lessen the computation requirement. The experimental results reveal that the proposed technique does not only improves the efficiency of SVM but also makes it less computationally expensive in terms of both computation time and memory utilization.
Authors and Affiliations
A. B. Adetunji, J. P. Oguntoye, O. D. Fenwa, E. O. Omidiora
Keywords
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- EP ID EP370499
- DOI 10.9790/0853-1704043544.
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