Abstract
In this paper, we investigate the effect of some illumination normalization techniques on a simple linear subspace face recognition model using two distance metrics on three challenging, yet interesting databases. The research takes the form of experimentation and analysis in which five illumination normalization techniques were compared and analyzed using two different distance metrics. The performances and execution times of the various techniques were recorded and measured for accuracy and efficiency. The illumination normalization techniques were Gamma Intensity Correction (GIC), discrete Cosine Transform (DCT), Histogram Remapping using Normal distribution (HRN), Histogram Remapping using Log-normal distribution (HRL), and Anisotropic Smoothing technique (AS). Results showed that improved recognition rate was obtained when the right preprocessing method is applied to the appropriate database using the right classifier.
Highlights
Illumination and pose challenges have been the serious bottlenecks in face recognition algorithms
The preprocessing techniques are Gamma Intensity Correction (GIC), Discrete Cosine Transform (DCT), Histogram remapping with normal distribution (HRN), Histogram remapping with log-normal distribution (HRL) and Anisotropic smoothing technique (AS)
Other preprocessing techniques experimented with are Gamma Intensity Correction (GIC), Discrete Cosine transform (DCT), and Anisotropic Smoothing (AS)
Summary
Illumination and pose challenges have been the serious bottlenecks in face recognition algorithms. Different image preprocessing techniques for face recognition were proposed and experimented with. The sequence of execution of the proposed method includes the preprocessing step, PCA/LDA subspace [12], and cosine/Euclidean classifiers. To facilitate a comprehensive study and analysis, five different preprocessing techniques were implemented on the PCA/LDA model using two different classifiers. These preprocessing techniques were carefully chosen based on their popularity and recorded success. This yielded ten (10) set of experimentations on each of the three databases used. The preprocessing techniques are Gamma Intensity Correction (GIC), Discrete Cosine Transform (DCT), Histogram remapping with normal distribution (HRN), Histogram remapping with log-normal distribution (HRL) and Anisotropic smoothing technique (AS)
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