New approach to similarity detection by combining technique three-patch local binary patterns (TP-LBP) with support vector machine

Abstract

Recognition systems have received a lot of attention because of their various
uses in people's daily lives, for example in robotic intelligence, smart cameras,
security surveillance or even criminal identification. Determining the
similarity of faces by different face variations is based on robust algorithms.
The validation of our experiment is done on two sets of data. In this paper, we
compare two facial recognition system techniques according to the
recognition rate and the average authentication time: in order to increase the
accuracy rate and decrease the processing time. our approach is based on
feature extraction by two algorithms principal components analysis scaleinvariant feature transform (PCA-SIFT) and speeded up robust features (SURF), then uses the random sample consensus (RANSAC) technique to cancel outliers. Finally, face recognition is established on the basis of proximity determination. The second technique is based on the association of support vector machine (SVM) classifier with the key point recovery technique. the results obtained by the second technique is better for both databases: The recognition rate of the base olivetti research laboratory (ORL) should be 98.125800 and that of the Grimace base 97.2851500. The evaluation according to the time of the second technique does not exceed 300ms on average.