GOLD Features with Machine Learning for Face Recognition

Abstract

Face detection and recognition is a technology in which machines intelligently recognize a human from his face. Face recognition is widely used for many applications such as tracking attendance, fraud detection for passport, in banks, Facebook’s Deep face for automatic tagging of people in photos etc. The initial steps in face recognition is feature extraction. Most of the commonly used feature extraction techniques are SIFT, SURF, Linear Discriminant Analysis. In this paper we have experimented with the use of Gaussian of local descriptors (GOLD) for face recognition. An optimal size of 8384 GOLD descriptors are obtained for every image. Classification models such as Naive Bayes, K nearest neighbor, Regression trees and multi-class ECOC are used to train with the descriptors and test the same. The dataset is partitioned in 70:30 ratio for every subject for training and testing respectively. We have used the ORL, Faces-95 and Grimace dataset to test our experimentation. From this, it was found that K nearest neighbor outperforms the others with the recall and precision of 99%.