diff --git a/face_recog_model/.ipynb_checkpoints/demo2-checkpoint.ipynb b/face_recog_model/.ipynb_checkpoints/demo2-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7198c4ba718f31d78e001665f36324151590936f
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+++ b/face_recog_model/.ipynb_checkpoints/demo2-checkpoint.ipynb
@@ -0,0 +1,447 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "private-audio",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "import shutil\n",
+    "import zipfile\n",
+    "import cv2\n",
+    "import os\n",
+    "import PIL\n",
+    "import numpy as np\n",
+    "import splitfolders\n",
+    "from PIL import Image\n",
+    "from mtcnn.mtcnn import MTCNN\n",
+    "from tensorflow.keras.preprocessing import image\n",
+    "from tensorflow.keras import layers\n",
+    "from tensorflow.keras.applications import vgg16\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "crucial-peoples",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector = MTCNN()\n",
+    "path_to_data = \"./data/\" \n",
+    "path_to_processed_data = \"./processed_datasets_new/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "raising-elder",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_face(filename, required_size=(224,224)):\n",
+    "    # load image from file\n",
+    "    img_to_load = plt.imread(filename)\n",
+    "    \n",
+    "    # detect face in the image using face detector\n",
+    "    results = detector.detect_faces(img_to_load)\n",
+    "    \n",
+    "    # extract the bounding box from the first face\n",
+    "    x1, y1, width, height = results[0]['box']\n",
+    "    x1, y1 = abs(x1), abs(y1)\n",
+    "    x2, y2 = x1 + width, y1 + height\n",
+    "    \n",
+    "    # extract the face\n",
+    "    face = img_to_load[y1:y2, x1:x2]\n",
+    "    \n",
+    "    # resize pixels to the model size\n",
+    "    image = Image.fromarray(face)\n",
+    "    image = image.resize(required_size)\n",
+    "    face_array = np.asarray(image)\n",
+    "    \n",
+    "    return face_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "north-coalition",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_rows, img_cols = 224, 224"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fatal-border",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = vgg16.VGG16(weights='imagenet', include_top = False,\n",
+    "                   input_shape = (img_rows, img_cols, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "public-flush",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for layer in model.layers:\n",
+    "    layer.trainable = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "seeing-insulin",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def addToModel(bottom_model, num_classes):\n",
+    "    top_model = bottom_model.output\n",
+    "    top_model = Flatten(name=\"flatten\")(top_model)\n",
+    "    top_model = Dense(1024, activation='relu')(top_model)\n",
+    "    top_model = Dense(1024, activation='relu')(top_model)\n",
+    "    top_model = Dense(512, activation='relu')(top_model)\n",
+    "    top_model = Dense(num_classes, activation='softmax')(top_model)\n",
+    "    return top_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "historic-scout",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, GlobalAveragePooling2D\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D\n",
+    "from keras.layers.normalization import BatchNormalization\n",
+    "from keras.models import Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "framed-decline",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"functional_7\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_4 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
+      "_________________________________________________________________\n",
+      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
+      "_________________________________________________________________\n",
+      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
+      "_________________________________________________________________\n",
+      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
+      "_________________________________________________________________\n",
+      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
+      "_________________________________________________________________\n",
+      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
+      "_________________________________________________________________\n",
+      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
+      "_________________________________________________________________\n",
+      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
+      "_________________________________________________________________\n",
+      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
+      "_________________________________________________________________\n",
+      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
+      "_________________________________________________________________\n",
+      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
+      "_________________________________________________________________\n",
+      "flatten (Flatten)            (None, 25088)             0         \n",
+      "_________________________________________________________________\n",
+      "dense_7 (Dense)              (None, 1024)              25691136  \n",
+      "_________________________________________________________________\n",
+      "dense_8 (Dense)              (None, 1024)              1049600   \n",
+      "_________________________________________________________________\n",
+      "dense_9 (Dense)              (None, 512)               524800    \n",
+      "_________________________________________________________________\n",
+      "dense_10 (Dense)             (None, 3)                 1539      \n",
+      "=================================================================\n",
+      "Total params: 41,981,763\n",
+      "Trainable params: 27,267,075\n",
+      "Non-trainable params: 14,714,688\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "FC_Head = addToModel(model, num_classes)\n",
+    "new_model = Model(inputs = model.input, outputs = FC_Head)\n",
+    "\n",
+    "print(new_model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "diverse-testament",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 74 images belonging to 3 classes.\n",
+      "Found 22 images belonging to 3 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_data_dir = './output/train'\n",
+    "validation_data_dir = './output/val'\n",
+    "\n",
+    "train_datagen = ImageDataGenerator(rescale=1./255, \n",
+    "                                  rotation_range=20,\n",
+    "                                  width_shift_range=0.2,\n",
+    "                                  horizontal_flip=True,\n",
+    "                                  fill_mode='nearest')\n",
+    "\n",
+    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
+    "\n",
+    "batch_size = 5\n",
+    "\n",
+    "train_generator = train_datagen.flow_from_directory(train_data_dir,\n",
+    "                                                   target_size=(img_rows, img_cols), \n",
+    "                                                   batch_size = batch_size, \n",
+    "                                                   class_mode = 'categorical')\n",
+    "\n",
+    "validation_generator = validation_datagen.flow_from_directory(validation_data_dir, \n",
+    "                                                              target_size=(img_rows, img_cols), \n",
+    "                                                              batch_size = batch_size, \n",
+    "                                                              class_mode = 'categorical')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "smoking-worcester",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.optimizers import RMSprop\n",
+    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
+    "\n",
+    "checkpoint = ModelCheckpoint(\"facedetect.h5\", \n",
+    "                            monitor=\"val_loss\", \n",
+    "                            mode=\"min\", \n",
+    "                            save_best_only=True,\n",
+    "                            verbose=1)\n",
+    "\n",
+    "earlystop = EarlyStopping(monitor=\"val_loss\", \n",
+    "                         min_delta=0, \n",
+    "                         patience=3,\n",
+    "                         verbose=1, \n",
+    "                         restore_best_weights = True)\n",
+    "\n",
+    "callbacks = [earlystop, checkpoint]\n",
+    "\n",
+    "new_model.compile(loss = 'categorical_crossentropy', \n",
+    "                 optimizer = RMSprop(lr=0.001), \n",
+    "                 metrics = ['accuracy'])\n",
+    "\n",
+    "nb_train_samples = 69\n",
+    "nb_validation_samples = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "veterinary-analysis",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "13/13 [==============================] - ETA: 0s - loss: 19.0991 - accuracy: 0.3538\n",
+      "Epoch 00001: val_loss improved from inf to 1.64605, saving model to facedetect.h5\n",
+      "13/13 [==============================] - 10s 794ms/step - loss: 19.0991 - accuracy: 0.3538 - val_loss: 1.6460 - val_accuracy: 0.6500\n",
+      "Epoch 2/10\n",
+      "13/13 [==============================] - ETA: 0s - loss: 0.2662 - accuracy: 0.9062\n",
+      "Epoch 00002: val_loss improved from 1.64605 to 0.00264, saving model to facedetect.h5\n",
+      "13/13 [==============================] - 10s 777ms/step - loss: 0.2662 - accuracy: 0.9062 - val_loss: 0.0026 - val_accuracy: 1.0000\n",
+      "Epoch 3/10\n",
+      "13/13 [==============================] - ETA: 0s - loss: 9.1471e-04 - accuracy: 1.0000\n",
+      "Epoch 00003: val_loss did not improve from 0.00264\n",
+      "13/13 [==============================] - 10s 791ms/step - loss: 9.1471e-04 - accuracy: 1.0000 - val_loss: 0.0151 - val_accuracy: 1.0000\n",
+      "Epoch 4/10\n",
+      "13/13 [==============================] - ETA: 0s - loss: 1.6128e-04 - accuracy: 1.0000\n",
+      "Epoch 00004: val_loss improved from 0.00264 to 0.00005, saving model to facedetect.h5\n",
+      "13/13 [==============================] - 11s 858ms/step - loss: 1.6128e-04 - accuracy: 1.0000 - val_loss: 4.6127e-05 - val_accuracy: 1.0000\n",
+      "Epoch 5/10\n",
+      "13/13 [==============================] - ETA: 0s - loss: 9.2999e-06 - accuracy: 1.0000\n",
+      "Epoch 00005: val_loss did not improve from 0.00005\n",
+      "13/13 [==============================] - 11s 810ms/step - loss: 9.2999e-06 - accuracy: 1.0000 - val_loss: 5.9016e-04 - val_accuracy: 1.0000\n",
+      "Epoch 6/10\n",
+      "13/13 [==============================] - ETA: 0s - loss: 5.3178e-06 - accuracy: 1.0000\n",
+      "Epoch 00006: val_loss did not improve from 0.00005\n",
+      "13/13 [==============================] - 10s 798ms/step - loss: 5.3178e-06 - accuracy: 1.0000 - val_loss: 7.8792e-04 - val_accuracy: 1.0000\n",
+      "Epoch 7/10\n",
+      "13/13 [==============================] - ETA: 0s - loss: 3.3397e-06 - accuracy: 1.0000Restoring model weights from the end of the best epoch.\n",
+      "\n",
+      "Epoch 00007: val_loss did not improve from 0.00005\n",
+      "13/13 [==============================] - 11s 810ms/step - loss: 3.3397e-06 - accuracy: 1.0000 - val_loss: 7.3239e-04 - val_accuracy: 1.0000\n",
+      "Epoch 00007: early stopping\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = new_model.fit(train_generator, \n",
+    "                        steps_per_epoch = nb_train_samples // batch_size, \n",
+    "                        epochs = 10, \n",
+    "                        callbacks = callbacks, \n",
+    "                        validation_data = validation_generator, \n",
+    "                        validation_steps = nb_validation_samples // batch_size\n",
+    "                       )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "advisory-examination",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "acc = history.history['accuracy']\n",
+    "val_acc = history.history['val_accuracy']\n",
+    "\n",
+    "loss = history.history['loss']\n",
+    "val_loss = history.history['val_loss']\n",
+    "\n",
+    "epochs_range = range(10)\n",
+    "\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
+    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
+    "plt.legend(loc='lower right')\n",
+    "plt.title('Training and Validation Accuracy')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(epochs_range, loss, label='Training Loss')\n",
+    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.title('Training and Validation Loss')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "characteristic-german",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for img in os.scandir('./test_images2'):\n",
+    "    print(img.path)\n",
+    "    img = extract_face(img.path)\n",
+    "#     img = image.load_img(img.path, target_size=(224,224,3))\n",
+    "    plt.imshow(img)\n",
+    "    plt.show()\n",
+    "    \n",
+    "    img_array = keras.preprocessing.image.img_to_array(img)\n",
+    "    img_array = tf.expand_dims(img_array, 0) # Create a batch\n",
+    "\n",
+    "    predictions = new_model.predict(img_array)\n",
+    "    score = tf.nn.softmax(predictions[0])\n",
+    "    print(score)\n",
+    "\n",
+    "    print(\n",
+    "        \"This image most likely belongs to {} with a {:.2f} percent confidence.\"\n",
+    "        .format(np.argmax(score), 100 * np.max(score))\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "public-deadline",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "intense-batman",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/face_recog_model/.ipynb_checkpoints/face_extraction-checkpoint.ipynb b/face_recog_model/.ipynb_checkpoints/face_extraction-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4a08ebfd0ea996a6892dfc9ec6bae5d40f7617d3
--- /dev/null
+++ b/face_recog_model/.ipynb_checkpoints/face_extraction-checkpoint.ipynb
@@ -0,0 +1,284 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "digital-security",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "import shutil\n",
+    "import zipfile\n",
+    "import cv2\n",
+    "import os\n",
+    "import PIL\n",
+    "import numpy as np\n",
+    "import splitfolders\n",
+    "from PIL import Image\n",
+    "from mtcnn.mtcnn import MTCNN\n",
+    "from tensorflow.keras.preprocessing import image\n",
+    "from tensorflow.keras import layers\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "norwegian-taylor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector = MTCNN()\n",
+    "path_to_data = \"./data/\" \n",
+    "path_to_processed_data = \"./processed_datasets_new/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "banned-exhibit",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x2097bffe8e0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "img = cv2.cvtColor(cv2.imread('data/lookes_wong/IMG_5596.jpg'), cv2.COLOR_BGR2RGB)\n",
+    "plt.imshow(img)\n",
+    "\n",
+    "# img = image.load_img('data\\IMG_5596.png', target_size=(224,224,3))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ordinary-strip",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector = MTCNN()\n",
+    "path_to_data = \"./data/\" \n",
+    "path_to_processed_data = \"./processed_datasets_new/\"\n",
+    "\n",
+    "def extract_face(filename, required_size=(224,224)):\n",
+    "    # load image from file\n",
+    "    img_to_load = plt.imread(filename)\n",
+    "    \n",
+    "    # detect face in the image using face detector\n",
+    "    results = detector.detect_faces(img_to_load)\n",
+    "    \n",
+    "    # extract the bounding box from the first face\n",
+    "    x1, y1, width, height = results[0]['box']\n",
+    "    x1, y1 = abs(x1), abs(y1)\n",
+    "    x2, y2 = x1 + width, y1 + height\n",
+    "    \n",
+    "    # extract the face\n",
+    "    face = img_to_load[y1:y2, x1:x2]\n",
+    "    \n",
+    "    # resize pixels to the model size\n",
+    "    image = Image.fromarray(face)\n",
+    "    image = image.resize(required_size)\n",
+    "    face_array = np.asarray(image)\n",
+    "    \n",
+    "    return face_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "virtual-methodology",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cropped_img = extract_face('data/lookes_wong/IMG_5596.jpg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "present-munich",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x20900d93700>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(cropped_img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "downtown-socket",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "if os.path.exists(path_to_processed_data):\n",
+    "    shutil.rmtree(path_to_processed_data)\n",
+    "os.mkdir(path_to_processed_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "minus-unemployment",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def check_if_processed_path_exists():\n",
+    "    if os.path.exists(path_to_processed_data):\n",
+    "        shutil.rmtree(path_to_processed_data)\n",
+    "    os.mkdir(path_to_processed_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "decimal-finding",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_image_to_cropped_face(path_to_data):\n",
+    "    \n",
+    "    for img_dir in os.scandir(path_to_data):\n",
+    "        count = 0\n",
+    "        img_dir_path = img_dir.path\n",
+    "        data_name = img_dir_path.split('/')[-1]  # variable to store data's name\n",
+    "        \n",
+    "        for entry in os.scandir(img_dir):\n",
+    "            result = extract_face(entry.path) # extract face for every image\n",
+    "            if result is not None:\n",
+    "                processed_folder = path_to_processed_data + data_name\n",
+    "                if not os.path.exists(processed_folder):\n",
+    "                    os.makedirs(processed_folder)\n",
+    "                    print(\"Generating cropped images in folder: \", processed_folder)\n",
+    "                \n",
+    "                cropped_file_name = data_name + str(count) + \".png\"\n",
+    "                cropped_file_path = processed_folder + \"/\" + cropped_file_name\n",
+    "                \n",
+    "                # save image to a new folder\n",
+    "                cv2.imwrite(cropped_file_path, result)\n",
+    "                count += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "qualified-power",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating cropped images in folder:  ./processed_datasets_new/lookes_wong\n",
+      "Generating cropped images in folder:  ./processed_datasets_new/loovik\n",
+      "Generating cropped images in folder:  ./processed_datasets_new/small_liang\n"
+     ]
+    }
+   ],
+   "source": [
+    "process_image_to_cropped_face(\"./data/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "fundamental-miller",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def split_data(data_path):\n",
+    "    splitfolders.ratio(data_path, output=\"output\", seed=1337, ratio=(.8, .2), group_prefix=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "viral-german",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Copying files: 90 files [00:00, 227.83 files/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "split_data(path_to_processed_data)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/face_recog_model/demo2.ipynb b/face_recog_model/demo2.ipynb
index 61c7f030338dfda33f29a815d59c92611d7a9221..7198c4ba718f31d78e001665f36324151590936f 100644
--- a/face_recog_model/demo2.ipynb
+++ b/face_recog_model/demo2.ipynb
@@ -333,9 +333,7 @@
     "                        callbacks = callbacks, \n",
     "                        validation_data = validation_generator, \n",
     "                        validation_steps = nb_validation_samples // batch_size\n",
-    "                       )\n",
-    "\n",
-    "# new_model.save(\"face_detect2.h5\")"
+    "                       )"
    ]
   },
   {
@@ -414,9 +412,7 @@
    "id": "public-deadline",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "train_generator.class_indices"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
diff --git a/face_recog_model/face_extraction.ipynb b/face_recog_model/face_extraction.ipynb
index c43160cb2cf5cdc26981c5ba7a8e3d76b629d31a..4a08ebfd0ea996a6892dfc9ec6bae5d40f7617d3 100644
--- a/face_recog_model/face_extraction.ipynb
+++ b/face_recog_model/face_extraction.ipynb
@@ -228,27 +228,6 @@
     "process_image_to_cropped_face(\"./data/\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "tropical-taylor",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "lookes_wong\n",
-      "loovik\n",
-      "small_liang\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in os.listdir(path_to_processed_data):\n",
-    "    print(i)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 12,
@@ -279,38 +258,6 @@
    "source": [
     "split_data(path_to_processed_data)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "joined-municipality",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "./test_images2/lookes1.jpg\n",
-      "./test_images2/lookes2.jpg\n",
-      "./test_images2/loovik1.jpg\n",
-      "./test_images2/loovik2.jpg\n",
-      "./test_images2/smallliang1.JPG\n",
-      "./test_images2/smallliang2.JPG\n"
-     ]
-    }
-   ],
-   "source": [
-    "for img_dir in os.scandir('./test_images2/'):\n",
-    "    print(img_dir.path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "tracked-blond",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {