A Comparison of PCA and HOG for Feature Extraction and Classification of
Human Faces
Abstract
This paper presents a simple analysis and comparison of the efficacy of
feature extraction and generalization achieved using Histogram of
Oriented Gradients (HOG) and Principal Component Analysis (PCA). A Naı̈ve
Bayes classifier was trained with a dataset of pre-classified human male
and female face images. The classifier was then presented with a fresh
set of faces and was given the task of classifying the images as male or
female. Also attempted, was an experiment of testing using the same
images used for training. It was observed that PCA was more adaptive
than HOG, in identifying new faces. HOG was more accurate in identifying
the same faces, provided there were sufficient training samples. In all,
PCA was found to be a better feature extractor, since it did not require
many training examples. It only mattered that a sufficient number of
principal components were used to obtain good classification accuracy.