Face Expression Analysis

Computer systems have opened up new horizons for emotion detection by recognizing and detecting nonverbal cues via automated devices. Although these systems are still far from achieving the capacity of human perception, they are able to classify and assess user emotions through predetermined mathema

Project Title

Face Expression Analysis

Project Area of Specialization

Artificial Intelligence

Project Summary

Computer systems have opened up new horizons for emotion detection by recognizing and detecting nonverbal cues via automated devices. Although these systems are still far from achieving the capacity of human perception, they are able to classify and assess user emotions through predetermined mathematical models with limited human intervention. Among the various modes of nonverbal communication, we focus on facial expressions which are captured by small cameras and later analyzed with computer software. Before introducing our proposed method in detail, the following is a brief overview of the advantages of using technology to replace more traditional forms of measurement discussed earlier.

FEA is a method of classifying facial motion and facial feature deformation into abstract classes based on visual information alone. It is a bottom-up approach in which the correlation between facial movement and a particular output (e.g., behavior, attitude, or emotion) becomes the formula for establishing not just a classification model but a prediction model as well. As all of the raw data measuring the precise movement of facial features are processed to create these models, they reflect intricate combinations of feature movements and movement patterns which would be lost on the human coder using conventional measurement schemes.

Computer vision and machine learning form the cornerstones of our approach to modeling and predicting human behavior. Machine learning is a technique in which computers are programmed to modify and improve their performance based on novel data input. In this way the machines can mimic the process of learning, autonomously adapting to external change in the environment (i.e., new input factors) and reducing the need for constant redesign.

Many of the problems that can be approached with machine learning are linearly separable, which means that there is a clear distinction between one group and another in a given space (e.g., cats on the left side, dogs on the right side). In this case, a model can be created to perform classifications in which parameters are established and modified until an optimal standard for categorizing different objects into a certain space is set. That is, through repeated trial and error, the model improves the method of calculating and estimating how to correctly classify individual instances of data until it reaches its optimum performance level. This is considered to be the training process through which these machine algorithms learn.

Project Objectives

We are surrounded nowadays with amazing technology that can help us to plan our day. But despite advancement in hardware and software, these devices that surround us are oblivious to how we feel, and they can't respond naturally. Face Expression Analysis (FEA) will predict gender and human expressions (a person is feeling sad, happy or angry). In FEA we will use images and live videos as input and getting successful output using machine learning techniques and results will show through the desktop application with the help of android mobile.

Project Implementation Method

We will use the Python programming language for training and testing our datasets. Publicly available datasets for facial expression analysis can be implemented. Machine learning, deep neural networks classification algorithm for prediction and Computer vision’s libraries for getting an accurate result.

Benefits of the Project

FEA can be implemented in different sectors of Education, Banking and Business, etc. where emotions play a major role.  In the future, we can make attendance system through FAE. FEA helps in attendance system reducing the paperwork, and it increases the security level. FEA can play a major role in reducing depression due to the burden of education over the student. It can help teachers predict through expressions from the student captured in cameras that how much of the lecture student understood.

Technical Details of Final Deliverable

FEA an approach for recognizing the category of facial expressions. Face Detection and Extraction of expressions from facial images is useful in many applications, such as robotics vision, video surveillance, digital cameras, security, and human-computer interaction. This breakthrough has been mainly enabled by the adoption of state-of-the-art computer vision and machine learning algorithms along with the gathering of high-quality databases of facial expressions all across the globe. These technologies use cameras embedded in laptops, tablets, and mobile phones or standalone webcams mounted to computer screens to capture videos of respondents as they are exposed to content of various categories.
The use of inexpensive webcams eliminates the requirement for specialized high-class devices, making automatic expression coding ideally suited to capture face videos in a wide variety of naturalistic environmental settings such as respondents’ homes, workplace, car, public transportation, and many more.

Final Deliverable of the Project

HW/SW integrated system

Type of Industry

Education , IT , Transportation , Security

Technologies

Artificial Intelligence(AI)

Sustainable Development Goals

Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com