Histogram of gradient (HOG)
This protocol is extracted from research article:
A novel periocular biometrics solution for authentication during Covid-19 pandemic situation
J Ambient Intell Humaniz Comput, Jan 3, 2021; DOI: 10.1007/s12652-020-02814-1

In the proposed approach HOG is used to extract the handcrafted features. HOG is a feature descriptor which is rotation invariance (since it is working on gradients) and its computational complexity is very low. HOG is selected in this study due to its ability to automatically handle some non-ideal situations. Feature extraction process using HOG is summarized in Algorithm 3.

Input: Image Dataset

Output: HOG feature vectors

Step 1: Consider an image ‘I’ from dataset.

Step 2: Convert the image into grayscale image.

Step 3: Calculate horizontal and vertical gradient value for every pixelof the image using the kernels as shown in Fig. 5.

Kernels to calculate horizontal and vertical gradient value in HOG

Step 4: Divide the image into adjacent and non-overlapping cells of p×p (p = 4) pixels, compute the histogram of orientation of gradients and binned them into ‘B’ bins (B = 9).

Step 5: There may be few pixels in the image whose orientation value may be close to bin boundary so, they might contribute to different bin also. To handle this situation, HOG uses weighted voting using bilinear interpolation and make fraction of the pixel’s gradient magnitude contribute to two bins.

Step 6: Group the cells into non-overlapping blocks which contained normalized gradient histogram.

Step 7: Normalized the block feature vectors and compute the HOG feature vector for the block.

Step 8: Concatenate all the features obtained from all the blocks to compute HOG feature vector for the image.

Step 9: Repeat step 1 to 8 for all the images in dataset.

Note: The content above has been extracted from a research article, so it may not display correctly.



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