Face emotion detection
As we all know in a classroom, the lectures delivered by the teacher sometimes become boring to some students. Some of them don?t even listen to the teacher and some students act like they are taking keen interest but they don?t. An experienced teacher knows all about it that which students are not
Project Title
Face emotion detection
Project Area of Specialization
Project Summary
As we all know in a classroom, the lectures delivered by the teacher sometimes become boring to some students. Some of them don’t even listen to the teacher and some students act like they are taking keen interest but they don’t. An experienced teacher knows all about it that which students are not taking interest in current lecture. So, developing a software that detects the emotions of the students and predict which students are taking interest according to the behavior of their facial expression can be helpful. We tackled the problem of recognizing the emotion of a student from an image of their facial expression. First, we built models capable of recognizing seven emotions (happy, sad, angry, afraid, surprise, disgust, and neutral). So, based on these emotions the system will detect how much particular student is taking interest in the lecture, and which students are not taking interest so that the teacher can work on those students respectively. In today’s context, video cameras can be easily accessed by everyone. These video cameras can be mobile-based cameras or other static cameras like surveillance cameras, smartphone cameras, Raspberry-Pi cameras, or laptops, etc. With the help of these cameras, it is easy to capture human faces from any location at any place.
Project Objectives
The objective of emotion recognition is identifying emotions of a human. The emotion can be captured either from face or from verbal communication. In this work we focus on identifying human emotion from facial expressions. Facial emotion recognition is one of the useful tasks and can be used as a base for many real-time applications. It can be used as a part of many interesting and useful applications like Monitoring security, treating patients in medical field, marketing research, E-learning etc. We humans can easily identify the emotion of other humans without any effort. Automatic detection of emotion of a human face is important due to its use in real-time applications.
Project Implementation Method
For this purpose, we will be using Neural networks and deep learning. Neural networks help us to made such a system that detects emotions and identify faces of students. We will train the model and use methodologies. The language we going to use is Python, we would use:
? Neural Networks and Deep learning
? OpenCV
? TensorFlow
? NumPy
? Tkinter
? haarcascade_frontalface
Benefits of the Project
As we all know in a classroom, the lectures delivered by the teacher sometimes become boring to some students. Some of them don’t even listen to the teacher and some students act like they are taking keen interest but they don’t. An experienced teacher knows all about it that which students are not taking interest in current lecture. So, developing a software that detects the emotions of the students and predict which students are taking interest according to the behavior of their facial expression can be helpful. We tackled the problem of recognizing the emotion of a student from an image of their facial expression. First, we built models capable of recognizing seven emotions (happy, sad, angry, afraid, surprise, disgust, and neutral). So, based on these emotions the system will detect how much particular student is taking interest in the lecture, and which students are not taking interest so that the teacher can work on those students respectively. In today’s context, video cameras can be easily accessed by everyone. These video cameras can be mobile-based cameras or other static cameras like surveillance cameras, smartphone cameras, Raspberry-Pi cameras, or laptops, etc. With the help of these cameras, it is easy to capture human faces from any location at any place.
Technical Details of Final Deliverable
The language we going to use is Python, we would use:
? Neural Networks and Deep learning
? OpenCV
? TensorFlow
? NumPy
? Tkinter
? haarcascade_frontalface
Final Deliverable of the Project
Core Industry
Other Industries
Core Technology
Other Technologies
Sustainable Development Goals
Required Resources
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 0 |
If you need this project, please contact me on contact@adikhanofficial.com
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