Parallel face Recognition Model
Face Recognition model using Local Binary
a) Serial Implementation b) Parallel Implementation using OpenMP( with HPC ).
Comparison of Both Models and proving which one is faster.
Training Step : For each image in the dataset, the following steps are applied:
▹ I used each 3 x 3 window in the image is processed to extract an LBP code.
▹ For each pixel in an image ,comparison with its 8 neighbours (on its left-top,
left-middle, left-bottom, right-top, etc.).
▹ Where the centre pixel’s value is less than the neighbour’s value, “1”.
▹ These values are converted into a decimal value and we will get a LBP code
▹ Histogram is computed, over the pixels, of the frequency of each “number”
occurring (i.e., each combination of which pixels are smaller and which are
greater than the centre).
Testing step : Histogram for the test image are generated as described previously.
• Distance values between test image’s histogram and training histograms
• Closest training histogram to determine the image is selected.
• Results are displayed.
This project effeciently depicts that the parallel implementation using OpenMp of the LBP Algorithm(or any) decreases the processing time significantly as compared to its serial implementation.