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Local Binary Patterns (LBP)

Local Binary Patterns (LBP). Tsung-Yi Wu. Concept. Divide the examined window to cells (e.g. 16x16 pixels for each cell).

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Local Binary Patterns (LBP)

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  1. Local Binary Patterns(LBP) Tsung-Yi Wu

  2. Concept • Divide the examined window to cells (e.g. 16x16 pixels for each cell). • For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, left-middle, left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counter-clockwise.

  3. Concept • Where the center pixel's value is greater than the neighbor, write "1". Otherwise, write "0". This gives an 8-digit binary number (which is usually converted to decimal for convenience). • Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e., each combination of which pixels are smaller and which are greater than the center).

  4. Concept • Optionally normalize the histogram. • Concatenate normalized histograms of all cells. This gives the feature vector for the window.

  5. Concept • LBP Operations

  6. Concept • Illustration

  7. Multi-scale Block LBP • Shengcai Liao et al.

  8. Face Description • The basic methodology for LBP based face description proposed by Ahonen et al. (2006) is as follows: • The facial image is divided into local regions and LBP texture descriptors are extracted from each region independently. The descriptors are then concatenated to form a global description of the face

  9. Face Recognition with LBP • Steps • Build Gallery LBP Histograms • Build the Probe LBP Histogram • Histogram • The recognition is performed using a nearest neighbor classifier • in the computed feature space with Chi square as a dissimilarity measure.

  10. Face Recognition with LBP • Gallery and Probe Pictures

  11. Face Recognition with LBP • Gallery and Probe Pictures

  12. Face Recognition with LBP • Face Recognition with Decision Tree-based Local Binary Patterns

  13. Histogram Matching • Many similarity measures for histogram matching have been proposed • histogram intersection is used to measure the similarity between two histograms

  14. Histogram Matching • Chi square as a dissimilarity measure • Log-likelihood statistic • Weighted X 2

  15. References • http://www.scholarpedia.org/article/Local_Binary_Patterns • http://en.wikipedia.org/wiki/Local_binary_patterns • Learning Multi-scale Block Local Binary Patterns for Face Recognition • Face Recognition with Decision Tree-based Local Binary Patterns • Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor

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