Face Recognition - Python

Question

I am trying to recognize faces using basic component analysis (PCA) using python.

Now I can get the minimum Euclidean distance between the training images images and the input image input_image . Here is my code:

 import os from PIL import Image import numpy as np import glob import numpy.linalg as linalg #Step1: put database images into a 2D array filenames = glob.glob('C:\\Users\\me\\Downloads\\/*.pgm') filenames.sort() img = [Image.open(fn).convert('L').resize((90, 90)) for fn in filenames] images = np.asarray([np.array(im).flatten() for im in img]) #Step 2: find the mean image and the mean-shifted input images mean_image = images.mean(axis=0) shifted_images = images - mean_image #Step 3: Covariance c = np.asmatrix(shifted_images) * np.asmatrix(shifted_images.T) #Step 4: Sorted eigenvalues and eigenvectors eigenvalues,eigenvectors = linalg.eig(c) idx = np.argsort(-eigenvalues) eigenvalues = eigenvalues[idx] eigenvectors = eigenvectors[:, idx] #Step 5: Only keep the top 'num_eigenfaces' eigenvectors num_components = 20 eigenvalues = eigenvalues[0:num_components].copy() eigenvectors = eigenvectors[:, 0:num_components].copy() #Step 6: Finding weights w = eigenvectors.T * np.asmatrix(shifted_images) # check eigenvectors.T/eigenvectors #Step 7: Input image input_image = Image.open('C:\\Users\\me\\Test\\5.pgm').convert('L').resize((90, 90)) input_image = np.asarray(input_image).flatten() #Step 8: get the normalized image, covariance, # eigenvalues and eigenvectors for input image shifted_in = input_image - mean_image c = np.cov(input_image) cmat = c.reshape(1,1) eigenvalues_in, eigenvectors_in = linalg.eig(cmat) #Step 9: Find weights of input image w_in = eigenvectors_in.T * np.asmatrix(shifted_in) # check eigenvectors/eigenvectors_in #Step 10: Euclidean distance d = np.sqrt(np.sum(np.asarray(w - w_in)**2, axis=1)) idx = np.argmin(d) print idx 

My problem is that I want to return the image (or its index in the images array) with the minimum Euclidean distance , and not its index in the distance array d

Answer 1

I do not believe that you changed the order of saving images in w compared to images , so the idx from np.argmin(d) should be the same index as the images list, so

 images[idx] 

There must be an image that you want.

Sure,

 images[idx].shape 

will give (1800,) because it is still flattened. If you want to untie it, you can do:

 images[idx].reshape(90,90)