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Table of contents
1.
Introduction
2.
Local Binary Pattern
3.
The architecture of Local Binary Pattern
3.1.
Parameters
3.2.
Training the Algorithm
3.3.
Applying the Algorithm
3.4.
Extracting the Histogram
3.5.
Performing Face recognition
4.
Frequently Asked Questions
5.
Conclusion
Last Updated: Mar 27, 2024
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Local Binary Pattern Algorithm

Author Tashmit
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Introduction

A human brain can recognize faces automatically every day and practically with no effort. Nevertheless, the simple task has proven to be complex for a computer because it has multiple variables that can hinder the accuracy of the methods, such as illumination variation, low resolution, and occlusion.

In computers, face recognition is generally the task of recognizing a person based on their facial image. It has gained a lot of attention in the past few decades because of its high-quality cameras and videos.

Local Binary Pattern

A Local Binary pattern is an algorithm used to recognize a person's face. It combined statistical and structural methods and was first described in 1994. The Local Binary Pattern is a technique of local representation of a picture. It comprises relative values ​​by comparing each pixel with its neighboring pixels.

Source: Link

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The architecture of Local Binary Pattern

Now we will study the step-by-step architecture of the Local Binary Pattern.

Parameters

There are four parameters to proceed with the Local Binary Patterns Algorithm.

  • Neighbors: The number of samples to build the circular local binary pattern. But make sure the more sample points you include, the higher the computational cost. It is usually set to 8.
  • Radius: The radius is used to create the circular local binary pattern and represent the radius around the central pixel. It is generally set to 1.
  • Grid X: It is the number of cells in the horizontal direction. The more cells, the finer the grid, and the higher the dimensionality of the resulting feature vector. It is usually set to 8.
  • Grid Y: It is the number of cells in the vertical direction. The more cells, the finer the grid, and the higher the dimensionality of the resulting feature vector. It is usually set to 8.

Training the Algorithm

After understanding the parameters, the next step is to train the algorithm; we need to gain a dataset that contains the facial images of the person we want to recognize. Also, we need to set an ID, it could be a number or the person's name, for each picture, so the algorithm will use the ID to recognize an input image and give an output. Photos of the same person must have the same ID.

Applying the Algorithm

To execute the Local Binary Pattern Algorithm, we would have to create an intermediate image that describes the original image better by highlighting the facial characteristics. The Local Binary Patterns algorithm uses a sliding window concept based on the radius and neighbors parameters.

Source: Link

Let us understand the application from the above image. 

First, a grayscale image will be divided into pixels. Let us understand the 3x3 window of the picture. The 3x3 window can be represented in the form of a matrix with the value of each pixel's intensity ranging from 0 to 255. Keep the central value as the threshold value and change the neighboring eight values to 1 if the intensity is greater than the threshold value and zero if the intensity is less than the threshold value. By doing this, we will get a binary matrix. Then, we would convert the binary value into a decimal value and set it to the central value of the matrix.

At the end of the Local Binary Patterns procedure, we have a new image that better represents the original image's characteristics.

 

Extracting the Histogram

The GridX and GridY parameters will be applied to the image generated in the last step to divide the picture into multiple grids.  

Source: Link

Since the extracted image is grayscale, each pixel will have a histogram containing 256 positions ranging from 0 to 255. We are supposed to concatenate the histograms to create a more significant and accurate final histogram. Thus the last histogram will represent the characteristics of the original image.

Performing Face recognition

In the final step, the algorithm is already trained. Each histogram created is used to represent each image from the training dataset. So, given an input image, we perform the steps again for this new image and create a histogram that best describes the picture.
Check out this problem - Largest Rectangle in Histogram. Also see, Sampling and Quantization

Frequently Asked Questions

  1. What is a Local Binary Pattern in machine learning?
    A Local Binary Pattern is a simple and efficient texture operator that labels the pixels of a picture by thresholding the neighborhood of each pixel and considers the result as a binary number.
     
  2. What are LBP features?
    Local Binary Pattern feature vector encodes local texture information, which you can use for classification, detection, and recognition tasks. 
     
  3. What types of image structures does LBP capture?
    Local Binary Pattern captures isotropic structural information. It completely fails in representing anisotropic information. 
     
  4. Can Python be used for facial recognition?
    OpenCV is the most popular library for computer vision. It was initially written in C/C++, and now it can provide bindings in Python.

Conclusion

In this article, we have extensively discussed the Local Binary Pattern Algorithm in Python. We hope that this blog has helped you enhance your knowledge regarding the Local Binary Pattern Algorithm and if you would like to learn more, check out our articles here

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