Predicting Mood from Facial Expressions

 

predicting Mood from Facial Expressions

 

Create AN AI MODEL TO DETECT FEELINGS

SOLVING A CLASSIFICATION PROBLEM

In this project, you will create an advanced AI model that is capable of detecting facial expressions to predict someone's mood. 

Mood Detection 

 

The problem

By taking on classification problems, humans can understand the world better. The application of this technique in the real-world is a rather more challenging task because of the fine lines between classes. However, AI makes classification despite these fine lines easier and faster.

The boost in AI’s capacity to do this has come from the improvements in computing power, the explosion of big data, and the advancements in deep learning models.

So much of human communication involves the face. By laughing, frowning, or crying, we show how we feel about the people and the events around us. Facial expressions are also crucial in areas like medical rehabilitation and behavioral studies.

In this project, you will develop an advanced model that is capable of detecting facial expressions and categorizing them into moods.


 

what is classification?

Although you will learn more about classification in the second course of Level2, Supervised Learning, in the AI Master Program, here’s a brief idea: classification is a technique under supervised learning in which a model takes in input, learns from the input, and then proceeds to classify new input it is fed with.  


 

learning outcomes

This project will teach you to:

  • Gather data and divide it into training and testing datasets.
  • Create and deploy an intelligent system that is capable of facial image recognition.
     

 

The data

Your ML model will be trained on a dataset of images of people with different facial expressions so that it can classify new facial expression images.

Categorize your dataset into training and testing datasets so that the accuracy of your model can be ascertained.

 

 

Project steps: 

1. Meet Your AI Identity 

Meet your AI Identity. You will teach your AI how to complete tasks as a human would. In due time, your AI Identity will grow more intelligent as you train it some more to take on different tasks with multiple projects. Eventually -in the future- you will be able to transfer your AI identity's intellect into a physical robot you can interact with in the real world.  

 

 

2. DEFINE THE PROBELM. START YOUR PROJECT

Begin every project by naming the project so you can keep track of what project it is exactly. Every project involves the steps of dataset preprocessing, model building, evaluation, and deployment.

By creating projects, working with others becomes more convenient and easier.

Click CREATE once you have added a project name and description. 


 

3. SELECT YOUR ML MODEL

Here, from the options of a Computer Vision model (MAKE ME SEE), Natural Language Processing model (MAKE ME READ), Voice Recognition (MAKE ME HEAR), and a tabular model (MAKE ME COUNT), you will select a Computer Vision model (MAKE ME SEE) to enable your model to classify facial expression images.

This model will enable you to train your AI transformer to categorize plant images according to their lifecycle.

Keep track of the newer models that will be added to the platform. Each one of these models will enable your AI transformer to take on and solve new problems.


 

4. COLLECT YOUR DATA

Your source can either be Kaggle or the Mood Recognition Facial Expressions dataset from our data library.

Take note that data gathering is a very crucial stage of an ML project with data preprocessing taking at least 70% of the time. Therefore, you will need to carry out this stage well.

 

 

5. TRAIN YOUR MODEL 

With your AI model trained now, it is time to test the model by using a new set of facial expression images. Be sure not to use an image that was used in the training phase. You will be cheating if you do.

Use the PREVIEW section to assess the performance of your model. Just upload the facial expression images from the testing dataset.

The confidence score will help you know well the model has been trained and if you are fine with it, export the model.

If you do not like the confidence score, return to step 4 and determine whether the image dataset used was sufficient in number.


 

6. TEST YOUR MODEL

Exporting your model is the next step after you are satisfied with the confidence score. You can export using our API into your website, application, robot, or any other machine.

Return to step 4 if you got a low confidence score. Doing this helps you review the training data class to be sure it is of high quality (clean) and quantity.

Your model is only as good as the data you feed it.


 

EXPORT YOUR MODEL

Exporting your model is the next step after you are satisfied with the confidence score. You can export using our API into your website, application, robot, or any other machine.

Return to step 4 if you got a low confidence score. Doing this helps you review the training data class to be sure it is of high quality (clean) and quantity.

Your model is only as good as the data you feed it.