DATA_Preprocessing ❄️

Data preprocessing for facial recognition using the Python Scikit-learn library

In this code we have added the following steps:

1. Define the number of key components to keep for the CPA using the variable “n_components”.

2. Initialize the PCA object using the Scikit-learn PCA() method.

3. For each image, apply CPA to the data after face alignment. To do this, we have added the following steps:

. Flatten the image into a 1D vector using the reshape() method.

. Apply PCR to flattened data using the fit() method.

. Transform flattened data using the transform() method.

. Return the transformed data to the 2D image form using the reshape() method.

4. Save the pre-processed image.

Note: The above code only covers part of the data preprocessing process for facial recognition, and it may be necessary to adjust the parameters to suit your specific needs.