Multi Block Local Binary Pattern Face Detection ~xRay Pixy
Multi-Block LBP is used to encode the rectangular region’s intensity by using local binary patterns. Local Binary Pattern (LBP) looks at nine pixels at a time (i.e., 3x3 window of image = 9-pixel values and 2^9 = 512 possible values). By using Local Binary Pattern we can turn this 3x3 matrix into a single value. LBP focus on the very local neighborhood like 3x3. Multi-block LBP has been used for larger-scale structures. With the help of Multi-Block Local Binary Pattern (MB-LBP), 256 types of different binary patterns can be formed for edge detection and face detection from still images.
Multi-Block Local Binary Pattern Algorithm Steps
Output = Detected Face.
Step 1. Upload a query image from the database.
Step 2. Conversion of the input image into a grayscale image.
Step 3. Divide the image into multiple blocks.
Step 4. Comparison of the neighbor pixel values with the central pixel value.
Step 5. If (Neighbor pixel value >= center pixel value) {
Assign 1;
}
Step 6. If (Neighbor pixel value < central pixel value) {
Assign 0;
}
Step 7. Generate a binary number starting from pixel 1 to 8.
Step 8. Convert the generated binary number into a decimal number.
Step 9. Apply Addition and multiply operation on the pixel values and store it in a variable.
Step 10. Selection of facial features from the query image.
Step 11. Apply CascadeObjectDetector on the selected facial features for face detection.
Step 12. Face Detected.
Step 1. Upload a query image from the database.
Step 2. Conversion of the input image into a grayscale image.
Step 3. Divide the image into multiple blocks.
Step 4. Comparison of the neighbor pixel values with the central pixel value.
Step 5. If (Neighbor pixel value >= center pixel value) {
Assign 1;
}
Step 6. If (Neighbor pixel value < central pixel value) {
Assign 0;
}
Step 7. Generate a binary number starting from pixel 1 to 8.
Step 8. Convert the generated binary number into a decimal number.
Step 9. Apply Addition and multiply operation on the pixel values and store it in a variable.
Step 10. Selection of facial features from the query image.
Step 11. Apply CascadeObjectDetector on the selected facial features for face detection.
Step 12. Face Detected.
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