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Welcome to LookSense, an innovative project delving into facial expression recognition using deep convolutional neural networks. Trained on the FER-2013 dataset from the International Conference on Machine Learning (ICML), LookSense accurately categorizes emotions in grayscale images, offering a robust tool for understanding and interpreting human

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LookSense - A Facial Expression Recognition

Introduction

Employing deep convolutional neural networks, this project seeks to categorize facial expressions into seven distinct emotions. Trained on the FER-2013 dataset, featured at the International Conference on Machine Learning (ICML), the dataset encompasses 35,887 grayscale images of 48x48 dimensions, each portraying one of seven emotions: anger, disgust, fear, happiness, neutrality, sadness, and surprise.

Dependencies

  • Python 3, OpenCV, TensorFlow
  • To install the required packages, run pip inistall -r requirements.txt.

Basic Usage

The repository is currently compatible with tensorflow-2.0 and makes use of the Keras API using the tensorflow.keras library.

  • First, clone the repository and enter the folder

    git clone https://github.com/sudoshivesh/LookSense.git
    cd LookSense
  • Download the FER-2013 dataset inside the src folder.

  • If you want to train this model, use:

    cd src
    python emotions.py --mode train
  • If you want to view the prediction without training again, I have already given a pre-trained model inside the src folder named as LookSense/src/model.h5 then run the command:

    cd src
    python emotions.py --mode display

*The folder structure is of the form: src:

  • data (folder)

  • dataset_prepare.py (file)

  • emotions.py (file)

  • haarcascade_frontalface_default.xml (file)

  • model.h5 (file)

  • By default, the system can recognize emotions on multiple faces within a live webcam stream. Achieving a test accuracy of 63.2% over 50 epochs, the implementation utilizes a straightforward 4-layer convolutional neural network (CNN) for efficient emotion detection.

  • The snapshot of the output is given below:

    codeInterface-output

    coding-interface

  • For any query, You may conatct Shivesh

About

Welcome to LookSense, an innovative project delving into facial expression recognition using deep convolutional neural networks. Trained on the FER-2013 dataset from the International Conference on Machine Learning (ICML), LookSense accurately categorizes emotions in grayscale images, offering a robust tool for understanding and interpreting human

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