Low-level features based 2D face recognition using machine learning

Abstract

In modern times, when deep learning-based face recognition is highly in demand, this paper presents machine learning techniques using the low-level feature extraction. Deep learning has a drawback of getting things done in a black-box, however extraction of low-level features viz. histogram of oriented gradients (HOG), speeded up robust features (SURF), and local binary patterns (LBPs) with a machine learning-based classification model presents higher simplicity. This paper presents the experimental demonstrations using 22 variations of machine learning models. Two face datasets, namely, Bosphorus and UMBDB are used for evaluating different classification models. Four experimentations are shown in the implementation section to demonstrate the effect of feature extraction, discretisation, feature variation, and noise in the image under probe. The subspace discriminant ensemble model yields the highest efficiency in classifying faces using HOG features. The effect of various noise attacks on the probe image is shown in the last experimentation.