How to do it...

1. Import the necessary packages:

import cv2 
import numpy as np   
from sklearn import preprocessing 

2. Load the encoding and decoding task operators:

class LabelEncoding(object): 
  # Method to encode labels from words to numbers 
  def encoding_labels(self, label_wordings): 
    self.le = preprocessing.LabelEncoder() 
    self.le.fit(label_wordings) 

3. Implement word-to-number conversion for the input label:

  def word_to_number(self, label_wordings): 
    return int(self.le.transform([label_wordings])[0]) 

4. Convert the input label from a number to word:

  def number_to_word(self, label_number): 
    return self.le.inverse_transform([label_number])[0] 

5. Extract images and labels from the input path:

def getting_images_and_labels(path_input): 
  label_wordings = [] 

6. Iterate the procedure for the input path and append the files:

  for roots, dirs, files in os.walk(path_input): 
    for fname in (x for x in files if x.endswith('.jpg')): 
      fpath = os.path.join(roots, fname) 
      label_wordings.append(fpath.split('/')[-2])

7. Initialize the variables and parse the input register:

  images = [] 
  le = LabelEncoding() 
  le.encoding_labels(label_wordings) 
  labels = [] 
  # Parse the input directory 
  for roots, dirs, files in os.walk(path_input): 
    for fname in (x for x in files if x.endswith('.jpg')): 
      fpath = os.path.join(roots, fname) 

8. Read the grayscale image:

      img = cv2.imread(fpath, 0)  

9. Extract the label:

      names = fpath.split('/')[-2] 

10. Perform face detection:

      face = faceCascade.detectMultiScale(img, 1.1, 2, minSize=(100,100)) 

11. Iterate the procedure with face rectangles:

      for (x, y, w, h) in face: 
        images.append(img[y:y+h, x:x+w]) 
        labels.append(le.word_to_number(names)) 
  return images, labels, le 
if __name__=='__main__': 
  path_cascade = "haarcascade_frontalface_alt.xml" 
  train_img_path = 'faces_dataset/train' 
  path_img_test = 'faces_dataset/test' 

12. Load the face cascade file:

  faceCascade = cv2.CascadeClassifier(path_cascade) 

13. Initialize face detection with local binary patterns:

  face_recognizer = cv2.createLBPHFaceRecognizer()

14. Extract the face features from the training face dataset:

  imgs, labels, le = getting_images_and_labels(train_img_path) 

15. Train the face detection system:

  print "nTraining..." 
  face_recognizer.train(imgs, np.array(labels)) 

16. Test the face detection system:

  print 'nPerforming prediction on test images...' 
  flag_stop = False 
  for roots, dirs, files in os.walk(path_img_test): 
    for fname in (x for x in files if x.endswith('.jpg')): 
      fpath = os.path.join(roots, fname) 

17. Validate the face recognition system:

      predicting_img = cv2.imread(fpath, 0) 
            # Detect faces 
      face = faceCascade.detectMultiScale(predicting_img, 1.1,  
                    2, minSize=(100,100)) 
            # Iterate through face rectangles 
      for (x, y, w, h) in face: 
        # Predict the output 
        index_predicted, config = face_recognizer.predict( 
predicting_img[y:y+h, x:x+w]) 
        # Convert to word label 
        person_predicted = le.number_to_word(index_predicted) 
        # Overlay text on the output image and display it 
        cv2.putText(predicting_img, 'Prediction: ' +  person_predicted,  
                        (10,60), cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 6) 
        cv2.imshow("Recognizing face", predicting_img) 
      a = cv2.waitKey(0) 
      if a == 27: 
        flag = True 
        break 
    if flag_stop: 
      break 

The face recognition output obtained is shown here: