Performance optimisation of face recognition based on LBP with SVM and random forest classifier

Face recognition requirements are well described, as many industrial applications rely on them to achieve one or more goals. The local binary pattern is used for feature extraction, and the support vector machine (SVM) classifier is used for classification. To recognise faces or objects, we first offer the system with a testing database for training purposes. After that, we send an object image to the system, and the system extracts only relevant information or a portion of the face and processes it using LBP and SVM. When the illumination of the object image varies, the accuracy of facial recognition drops, and when just one training sample is provided, it does not provide the best matching results. In this paper, we describe a model that works by extracting local binary patterns from distinct sample photos, training the SVM classifier for the same, and then categorising input probe images using binary and multiclass SVM. In this case, the accuracy for 80% training and 20% testing ratio is 97.5%.