Robust Illumination and Pose Invariant Face Recognition System using Support Vector Machines

摘要

The fundamental objective behind the denoising is to get rid of the noise while recollecting the significant signal features to the maximum possible extent. This issue seems to be very simple against the backdrop of realistic scenarios, where the category and quantity of noise, along with the noise and the kind of images all are variable parameters, and a solitary technique or approach is incompetent to yield reasonable results. There is a host of methods employed to eliminate the noise in images and carryout the classification procedure efficiently. In the innovative approach, at the outset, the images are shortlisted from the database, and thereafter the technique flows through the following three phases such as the pre-processing procedure, feature extraction procedure and the classification procedure by means of the Support Vector Machine (SVM). In the feature extraction procedure the Gray Level Co-occurrence Matrix (GLCM) traits like the autocorrelation, contrast, cluster prominence, cluster shade, dissimilarity, energy, area, homogeneity, perimeter, circularity and entropy are extracted. Subsequently, SVM is employed for the purpose of the face recognition, because the optimal separating hyper plane can be achieved easily after ascertaining the thinner product between feature vectors, which constitutes an exemplary quality of the SVM. The kernel functions are able to achieve only the inner product value in the feature space being unaware of the nonlinear mapping.