Deep CNN ensemble for recognition of face images

The paper considers the problem of recognition of face images using an ensemble of deep CNN networks. The solution combines different feature selection methods and three types of classifiers: support vector machine, a random forest of decision trees, and softmax built into the CNN classifier. Deep l...

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Bibliographic Details
Published in2021 22nd International Conference on Computational Problems of Electrical Engineering (CPEE) pp. 1 - 4
Main Authors Szmurlo, Robert, Osowski, Stanislaw
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.09.2021
Subjects
CNN
Decision trees
Deep learning
deep networks
Electrical engineering
ensemble of classifiers
Face recognition
Feature extraction
feature selection
Image recognition
Support vector machines
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Summary:The paper considers the problem of recognition of face images using an ensemble of deep CNN networks. The solution combines different feature selection methods and three types of classifiers: support vector machine, a random forest of decision trees, and softmax built into the CNN classifier. Deep learning fulfills an important role in the developed system. The numerical descriptors created in the last locally connected convolutional layer of CNN flattened to the form of a vector, are subject to four different selection mechanisms. Their results are delivered to the three classifiers which are the members of the ensemble. The developed system was tested on the problem of face recognition. The dataset was composed of 68 classes of greyscale images. The results of experiments have shown significant improvement of class recognition resulted from the application of the ensemble.
DOI:10.1109/CPEE54040.2021.9585253