Abstract
This article implements and analyzes face recognition algorithms based on fuzzy image representations using several main classifiers, including Decision Tree, Random Forest, and K-Nearest Neighbors. The main focus is on the extraction of informative features to correct recognition and classification of the human face. The algorithms under study recognize a person by their eyes if their face is covered with something. According to the research results, the accuracy of the KNN, Random Forest and Decision Tree algorithms is 95.5%, 96.1% and 92.3%, respectively. In order to improve the results, the ensemble learning method was implemented, which allows a combination of several classification results using a metaclassifier. The first level of ensembling uses the three basic models mentioned above to predict the output of the test datasets. In the second level of this process, a new result was generated, taking all the results of the underlying models as input. As a result of the ensembling, the accuracy was 96.8%, exceeding the accuracy of each algorithm. A comparative assessment of the developed ensemble algorithm quality with other ensemble algorithms based on the boosting approach is also made.