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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.)