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Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions. IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 6, JUNE 2010 Xiaoyang Tan and Bill Triggs 報告者:王克勤. Introduction. - PowerPoint PPT Presentation
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Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions
IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 6, JUNE 2010Xiaoyang Tan and Bill Triggs
報告者:王克勤
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Introduction
• Face recognition has received a great deal of attention from the scientific and industrial communities over the past several decades
• This paper focuses mainly on the issue of robustness to lighting variations
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Traditional approaches
• Appearance-based
• Normalization-based
• Feature-based
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Appearance-based approaches
• Training examples are collected under different lighting conditions and directly used to learn a global model of the possible illumination variations
• Direct learning of this kind makes few assumptions but it requires a large number of training images and an expressive feature set
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Normalization-based approaches
• Normalization based approaches seek to reduce the image to a more canonical form in which the illumination variations are suppressed
• Histogram equalization
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Histogram equalization
• A method in image processing of contrast adjustment using the image's histogram
http://en.wikipedia.org/wiki/Histogram_equalization
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Feature-based approaches
• Feature-based approaches extracts illumination-insensitive feature sets directly from the given image
• Local binary patterns(LBP)
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Local binary patterns(cont.)
1 2 4
8 16
32 64 128
1 1 1
1 0
1 0 0
LBP=1X1 + 1X2 + 1X4 + 1X8 + 1X32 =47
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• Appearance-based approaches• Normalization-based approaches• Feature-based approaches
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• Preprocessing chain• LTP local texture feature sets• Multiple-feature fusion framework
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Preprocessing chain
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Gamma correction
• Gamma correction is a nonlinear gray-level transformation
• Replace gray-level withor (for )
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Difference of Gaussian Filtering
• Gamma correction does not remove the influence of overall intensity gradients such as shading effects
• High-pass filtering removes both the useful and the incidental information
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Difference of Gaussian Filtering(cont.)
• Difference of Gaussians is a grayscale image enhancement algorithm that involves the subtraction of one blurred version of an original grayscale image from another, less blurred version of the original
• Difference of Gaussians can be utilized to increase the visibility of edges and other detail present in a digital imagehttp://en.wikipedia.org/wiki/Difference_of_Gaussians
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Masking
• If facial regions (hair style, beard, ) that are felt to be irrelevant or too variable need to be masked out, the mask should be applied at this point
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Contrast equalization
• This stage rescales the image intensities to standardize a robust measure of overall contrast or intensity variation
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Local ternary patterns
• Local binary patterns threshold at exactly the value of the central pixel tend to be sensitive to noise
• This section extends LBP to 3-valued codes, LTP
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Local ternary patterns(cont.)
The tolerance interval is [49, 59]
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Local ternary patterns(cont.)
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Local ternary patterns(cont.)