Face Expression Recognition + Live Webcam Detection
😃 😐 😒 🎥

Built with

How To Run Locally • Built process • Feedback

How To Run Locally

# Clone this repository
$ git clone https://github.com/boramorka/Emotion_detection_v1.git

# Go into the repository
$ cd Emotion_detection_v1

# Install dependencies
$ pip install requirements.txt

# Run app
$ python main.py

Make sure you have:

• Nvidia videocard
• Installed all requirement libraries
• Installed Nvidia Cuda
• Installed Nvidia drivers
• Installed cuDNN

Built process

• Dateset for training: https://www.kaggle.com/datasets/jonathanoheix/face-expression-recognition-dataset

• Import libraries:

  import matplotlib.pyplot as plt
  import numpy as np
  import pandas as pd
  import seaborn as sns
  import os
  
  # Importing Deep Learning Libraries
  
  from keras.preprocessing.image import load_img, img_to_array
  from keras.preprocessing.image import ImageDataGenerator
  from keras.layers import Dense,Input,Dropout,GlobalAveragePooling2D,Flatten,Conv2D,BatchNormalization,Activation,MaxPooling2D
  from keras.models import Model,Sequential
  from keras.optimizers import Adam,SGD,RMSprop

• Model architecture:

  model = Sequential()
  
  #1st CNN layer
  model.add(Conv2D(64,(3,3),padding = 'same',input_shape = (48,48,1)))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size = (2,2)))
  model.add(Dropout(0.25))
  
  #2nd CNN layer
  model.add(Conv2D(128,(5,5),padding = 'same'))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size = (2,2)))
  model.add(Dropout (0.25))
  
  #3rd CNN layer
  model.add(Conv2D(512,(3,3),padding = 'same'))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size = (2,2)))
  model.add(Dropout (0.25))
  
  #4th CNN layer
  model.add(Conv2D(512,(3,3), padding='same'))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.25))
  
  model.add(Flatten())
  
  #Fully connected 1st layer
  model.add(Dense(256))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(Dropout(0.25))
  
  # Fully connected layer 2nd layer
  model.add(Dense(512))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(Dropout(0.25))
  
  model.add(Dense(no_of_classes, activation='softmax'))

• The model has a 4.5 million parameters

• Now we can recognize 7 emotions

  • Angry
  • Disgust
  • Fear
  • Happy
  • Neutral
  • Sad
  • Surprise

• Creating main.py for capturing:

  """Capturing block"""
  
  cap = cv2.VideoCapture(0)
  
  while True:
      _, frame = cap.read()
      labels = []
      gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
      faces = face_classifier.detectMultiScale(gray)
  
      main_model()
  
      cv2.imshow('Emotion Detector',frame)
      if cv2.waitKey(1) & 0xFF == ord('q'):
          break

• Main model for face detection and putting text:

    def main_model():
      for (x,y,w,h) in faces:
        cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,255),2)
        roi_gray = gray[y:y+h,x:x+w]
        roi_gray = cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA)
  
  
  
        if np.sum([roi_gray])!=0:
            roi = roi_gray.astype('float')/255.0
            roi = img_to_array(roi)
            roi = np.expand_dims(roi,axis=0)
  
            prediction = classifier.predict(roi)[0]
            label=emotion_labels[prediction.argmax()]
            label_position = (x,y)
            cv2.putText(frame,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)
        else:
            cv2.putText(frame,'No Faces',(30,80),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)

Feedback

🤵 Feel free to send me feedback on Telegram. Feature requests are always welcome.

🧮 Check my other projects.