Robust Face Identification with Small Sample Sizes using Bag of Words and Histogram of Oriented Gradients

Face identification under small sample conditions is currently an active research area. In a case of very few reference samples, optimally exploiting the training data to make a model which has a low generalization error is an important challenge to create a robust face identification algorithm. In this paper we propose to combine the histogram of oriented gradients (HOG) and the bag of words (BOW) approach to use few training examples for robust face identification. In this HOG-BOW method, from every image many sub-images are first randomly cropped and given to the HOG feature extractor to compute many different feature vectors. Then these feature vectors are given to a K-means clustering algorithm to compute the centroids which serve as a codebook. This codebook is used by a sliding window to compute feature vectors for all training and test images. Finally, the feature vectors are fed into an L2 support vector machine to learn a linear model that will classify the test images. To show the efficiency of our method, we also experimented with two other feature extraction algorithms: HOG and the scale invariant feature transform (SIFT). All methods are compared on two well-known face image datasets with one to three training examples per person. The experimental results show that the HOG-BOW algorithm clearly outperforms the other methods.