Face recognition based on wavelet transformation and sparse feature extraction
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Abstract (EN)
In this study, the problem of recognition of human faces from frontal views with varying expression and illumination by using sparse signal represantation has been studied. Based on a sparse representation computed by l^1 – minimization, face recognition success rate has been analyzed. In this study, in addition to what is already accomplished in research literature, the effect of Wavelet Filter usage on face recognition has been investigated. The results have been examined and compared separately. The success of the Wavelet Filter in the face recognition system is shown by using tables and graphs. Using Wavelet Filter in addition to SRC have increased the success rate. The study was carried out with the AR database using the MATLAB programming software.
Author
Deniz Katipoğlu
Institution
How to Cite
Deniz Katipoğlu (Master Thesis). Face recognition based on wavelet transformation and sparse feature extraction, 2017, Atatürk University.
Keywords
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