Facial Expression Recognition using CNN Final Year Project product image (1)
Facial Expression Recognition using CNN Final Year Project product image (2)
Facial Expression Recognition using CNN Final Year Project product image (3)
Facial Expression Recognition using CNN Final Year Project product image (4)
Facial Expression Recognition using CNN Final Year Project product image (5)
Facial Expression Recognition using CNN Final Year Project product image (6)
Facial Expression Recognition using CNN Final Year Project product image (7)
Facial Expression Recognition using CNN Final Year Project product image (8)
Facial Expression Recognition using CNN Final Year Project product image (9)
Facial Expression Recognition using CNN Final Year Project product image (1)
Facial Expression Recognition using CNN Final Year Project product image (2)
Facial Expression Recognition using CNN Final Year Project product image (3)
Facial Expression Recognition using CNN Final Year Project product image (4)
Facial Expression Recognition using CNN Final Year Project product image (5)
Facial Expression Recognition using CNN Final Year Project product image (6)
Facial Expression Recognition using CNN Final Year Project product image (7)
Facial Expression Recognition using CNN Final Year Project product image (8)
Facial Expression Recognition using CNN Final Year Project product image (9)

Facial Expression Recognition using CNN Final Year Project

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The following contents are Downloadable immediately after the successful payment

  • Source Code

  • Project Report

  • PowerPoint Presentation (PPT)

  • Instructions to install the necessary Software and Libraries

  • Step-by-step instructions to execute the project

Project Description

Face Expression recognition is of great importance to real-world applications such as video conferences, human-machine interaction, and security systems. As compared to traditional machine learning approaches, deep learning-based methods have shown better performances in terms of accuracy and speed of processing in image recognition.

The main objective here is to classify each face based on the emotions shown into seven categories which include Anger, Disgust, Fear, Happiness, Sadness, Surprise, and Neutrality.

This project proposes a Modified Convolutional Neural Network (CNN) architecture by adding two normalization operations to two of the layers. The normalization operation which is batch normalization provided accelerates the network. CNN architecture is employed to extract distinctive face features and a Softmax classifier is used to classify faces in the fully connected layer of CNN. In the experiment part, the Georgia Tech Database shows that the proposed approach has improved the facial Expression recognition performance with better recognition results.

CNN Classifier is then used to classify images taken from the webcam on the Laptop. The face is detected in webcam frames using the Haar cascade classifier from OpenCV. Then detected face is cropped, normalized, and fed to the CNN Classifier.

Installation and Setup

 
Train the Model with Dataset FER-2013 Jupyter Notebook (provided GPU in your PC) else Google colab.
 
1) Extract the downloaded project and data set into your computer
 
2) Install Anaconda IDE
 
3) Install Tensorflow 2.10.0
follow the video to understand how to install Tensorflow 2.10.0
 

https://youtu.be/b9e3J-NJ8TY

 
4) Install OpenCV, Jupyter, matplotlib, scipy using the command in the tf environment
  • pip install opencv-python
  • pip install jupyter
  • pip install matplotlib
  • pip install scipy
 
5) To train the Model
Activate conda environment using the command -->> conda activate tf
 
Open Jupyter Notebook using the command -->> jupyter Notebook
 
Open Facial_Emotion_Recogination.ipynb
Run All cells
 
6) After successful training the model weights and trained models are saved in the current working directory
Model Weights - "fernet_bestweight.h5"
Trained Model  - "ferNetModel.h5"
 

Video Demonstration of Facial Expression Recognition using CNN Final Year Project

 

https://youtu.be/gIKm2PvAEhY

 

Still need help to set up and execute the project

  • Setup and modification are paid services based on requirements.