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Partial Face Images Classification Using Geometrical Features

  • Piotr MilczarskiEmail author
  • Zofia Stawska
  • Shane Dowdall
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 889)

Abstract

In the paper, we have focused on the problem of choosing the best set of features in the task of gender classification/recognition. Choosing a minimum set of features, that can give satisfactory results, is important in the case where only a part of the face is visible. Then, the minimum set of features can simplify the classification process to make it useful in video analysis, surveillance video analysis as well as for IoT and mobile applications. We propose four partial view areas and show that the classification accuracy is lower by maximum 5% than in using full view ones and we compare the results using 5 different classifiers (SVM, 3NN, C4.5, NN, Random Forrest) and 2 test sets of images. That is why the proposed areas might be used while classifying or recognizing veiled or partially hidden faces.

Keywords

Geometric facial features Image processing Surveillance video analysis Biometrics Gender classification Support vector machines K-Nearest neighbors Neural networks Decision tree Random forrest 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Physics and Applied InformaticsUniversity of LodzLodzPoland
  2. 2.Department of Visual and Human Centred ComputingDundalk Institute of TechnologyDundalk, Co. LouthIreland

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