This project it's about performing face recognition using LBPH. The reason for using this kind of techniques for computer vision its computational costs, I know machine learning its more precise, but takes a lot of time in order to produce results, moreover, needs big datasets.
For a more detailed explanation of how the algorithm it's implemented you can visit Facial Recognition with LBPH in Python
Local binary patterns (LBP) is a type of visual descriptor used for classification in computer vision. LBP is the particular case of the Texture Spectrum model proposed in 1990. LBP was first described in 1994.[1]
Three neighborhood examples used to define a texture and calculate a local binary pattern (LBP)
The LBP feature vector, in its simplest form, is created in the following manner:
- Divide the examined window into 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.
- Where the center pixel's value is greater than the neighbor's value, write "0". Otherwise, write "1". 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). This histogram can be seen as a 256-dimensional feature vector.
- Optionally normalize the histogram.
- Concatenate (normalized) histograms of all cells. This gives a feature vector for the entire window.
The LBP as a bank of filters
For optimization reasons the project uses the convolve function, in this case the lbp algorithm it's performed with a series of filters, and a convolve operation. The article that inspired this is [2].
Example of how LBP is calculated
LBP shown as filter bank
The LBP algorithm will perform the neighborhood filtering, resulting in something like this:
Original image
Image after LBP processing
Next, the image it's interpreted as a histogram with the levels of gray scale.
Histogram example
After all the images of the same subject are processed it's time to make a mean of all the histogram (this is called class histogram).
When we have calculated all the classes the model it's "trained" and you only need to compare a given image (not from the training set) with every class using a distance algorithm (Euclidean, Chi-square, Manhattan, etc) and select the minor distance to say that's the most possible identified subject.
Example of the identified subject (can be inaccurate)
With this algorithm the Precision is about 65-80%, with a tiny dataset of 150 images and 11 distinct subjects. With dataset greater the precision it's better (about 75-85%).
It's not a big precision rate, but this algorithm takes only a couple of minutes to perform the output in a decent PC, compared with machine learning techniques that can take half an hour and 1K image dataset at least I think it's not bad.
[1] T. Ojala, M. Pietikäinen, and D. Harwood (1994), "Performance evaluation of texture measures with classification based on Kullback discrimination of distributions", Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR 1994), vol. 1, pp. 582 - 585.
[2] devangini, «Local binary patterns(Lbp)», Devangini Patel, 03-jun-2016. [Online]. Disponible en: https://devanginiblog.wordpress.com/2016/06/03/local-binary-patterns-lbp/. [Accessed: 23-abr-2021]