Face recognition using interest points and ensemble of classifiers

In human beings, it is the responsibility of the temporal lobe of the brain for recognition of faces. Certain features of the face trigger the neurons of the temporal lobe which are then stored. These eventually lead to the identification of the face. In machine learning system, a huge dataset of images is used as a reference by the face recognition algorithms. The most prominent features are extracted from what is referred to as landmarks on the face and are analyzed based on their relative position amongst other features. The main challenge in computer vision lies in the identification and extraction of these features which in turn assist in face recognition owing to the exponentially large degree of variability that can be seen in appearances. Speed and accuracy are some of the most prominent challenges for the same. We explore ELM critically and study its performance. Owing to its extremely fast learning, it finds potential in real-time applications. The goal of this paper is to evaluate the performance of the extreme learning machine algorithm for facial recognition when used as a standalone model as well as when it is used with an ensemble learner. We then conclude with a voting classifier which is responsible for the final predictions made. Various combinations of ensembles are explored to come up with the best performing model.