new_contents: import openface
  import numpy as np
  import os
  import cv2
  import pickle
  
  id_name = ["Alec", "Greg", "Phong", "Emil"]
  
  
  def classify(aligned_face, net, clf, le):
      rep = net.forward(aligned_face)
      predictions = clf.predict_proba(rep.reshape((1, len(rep)))).ravel()
      maxI = np.argmax(predictions)
      person = le.inverse_transform(maxI)
      confidence = predictions[maxI]
      print("Predict {} with {:.2f} confidence.".format(person, confidence))
      return person
  
  # path to the face alignment model
  dLib_predictor = "../../resource/shaqpe_predictor_68_face_landmarks.dat"
  # construct the face alignment model
  align = openface.AlignDlib(dLib_predictor)
  # path to deep neural network for feature representation
  network_model = "../../resource/nn4.small2.v1.t7"
  # construct the network for feature represenation
  net = openface.TorchNeuralNet(network_model, 96)
  
  classifier_model = "../../resource/svm.pkl"
  
  with open(classifier_model, 'r') as f:
      (le, clf) = pickle.load(f)
      print("Successfully loaded SVM model")
      video = cv2.VideoCapture(0)
      if video is None:
          exit()
  
      while True:
          # grab image
          ret, cameraFrame = video.read()
          if not ret:
              exit()
          try:
              bbs = align.getAllFaceBoundingBoxes(cameraFrame)
              print("Found {} face".format(len(bbs)))
              for bb2 in bbs:
                  alignedFace = align.align(96, cameraFrame, bb2,
                                              landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)
                  id = classify(alignedFace, net, clf, le)
                  person_name = id_name[id]
                  print(person_name)
  
                  rectColor = (0,255, 0)
                  textColor = (255, 0, 0)
                  face_top_left = (bb2.left(), bb2.top())
                  face_bottom_right = (bb2.right(), bb2.bottom())
                  cv2.rectangle(cameraFrame, face_top_left, face_bottom_right, rectColor)
                  cv2.putText(cameraFrame, person_name, face_top_left,
                          fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=textColor, thickness=2)
  
              cv2.imshow('FaceRecognizer', cameraFrame)
              if cv2.waitKey(1) & 0xFF == ord('q'):
                  break
          except:
              cv2.imshow('FaceRecognize', cameraFrame)
              continue
  
subject: Use the model to classify face in local camera.