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Scientific Visualization
| Issue Year: | 2015 |
| Quarter: | 2 |
| Volume: | 8 |
| Number: | 2 |
| Pages: | 107 - 119 |
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| Article Name: |
ANALYSIS OF METHODS OF FEATURES EXTRACTION FROM IMAGE OF HUMAN FACE FOR IDENTIFICATION |
| Authors: |
Yu.V. Savitskiy (Russian Federation), S.G. Nebaba (Russian Federation), V.G. Spitsyn (Russian Federation), S.Yu. Andreev (Russian Federation), M.A. Makarov (Russian Federation) |
| Address: |
Yu.V. Savitskiy
mr-l-ik@yandex.ru
Institute of Cybernetics of the National Research Tomsk Polytechnic University, Russian Federation
S.G. Nebaba
stepan-lfx@mail.ru
Institute of Cybernetics of the National Research Tomsk Polytechnic University, Russian Federation
V.G. Spitsyn
vl.gr.sp@gmail.com
Institute of Cybernetics of the National Research Tomsk Polytechnic University, Russian Federation
S.Yu. Andreev
riftas@rambler.ru
Institute of Cybernetics of the National Research Tomsk Polytechnic University, Russian Federation
M.A. Makarov
makarovf@sibmail.com
Institute of Cybernetics of the National Research Tomsk Polytechnic University, Russian Federation |
| Abstract: |
This paper is just a part of the face recognition project which is being developed in Tomsk Polytechnic University. The main goal of that project is to build a system of face recognition in the video stream in real time. Several well-known methods of image processing such as filtering in the frequency domain, difference of Gaussians, Haar wavelet transform, Gabor filtering, log-Gabor filtering and feature extraction such as standard deviation, discrete cosine transform, Hu moments, histogram of oriented gradients that can be used for solving the problem of human face identification are described in this paper. Some ways of applying their combinations in feature vector extraction algorithms are presented. The library of computer vision OpenCV was used in our study. The effectiveness of the proposed algorithms was tested using Caltech Faces base. The results are showed in the comparison chart. The effectiveness comparison was based on the equal error rate, calculated for the false accept rate and false reject rate. The conclusion about the suitability of the most efficient algorithm for the identification problem summarizes this paper. Algorithm that consists of three steps: finding difference of Gaussians, log-Gabor filtering and standard deviation calculation found as the most efficient during this study.
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| Language: |
Russian |
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