Sentitect Emotion Classifier
An emotion Detection System is a modern approach towards the solution of human-computer interaction or robot-human interaction. While communicating, emotions are the first thing that is analyzed by a person or machines, and in order to recognize those expressions or emotions by machines, we would ha
2025-06-28 16:29:03 - Adil Khan
Sentitect Emotion Classifier
Project Area of Specialization Artificial IntelligenceProject SummaryAn emotion Detection System is a modern approach towards the solution of human-computer interaction or robot-human interaction. While communicating, emotions are the first thing that is analyzed by a person or machines, and in order to recognize those expressions or emotions by machines, we would have to design a system in such a way that machines can detect human emotions. The final deliverable will be in the form of web-based software in which the screen will be depicting the user's face, and a label will be highlighting their expressions.
Project ObjectivesOur project objective is to design an emotion detection system from which machines can easily interpret human emotions. To feed human emotions, we have to train machines according to our needs, and for that, we would be using deep learning to teach the machines about human emotions.
Project Implementation MethodTo cater to the emotion detection problem among machines, we would be building an emotion detection system that we will build on the framework of deep learning. We will train the machine with existent datasets of human emotions and will then compare it with real-time face detection. For this, we would be using Python as our basic language along with the CNN algorithm. The purpose of using CNN is its higher accuracy. The multi-layer algorithm refines the raw data to its meaningful form. CNN would be applied to real-time data that we would be collecting with the help of High Definition WebCam so that a clear picture can be captured. We would be using OpenCV software to map the face of real-time computer vision. The real-time data would then be compared with the pre-stored data set of human emotions. We would be taking 7 universally recognized human expressions i.e. happy, sad, angry, neutral, disgust, fear, and surprise. We would be then training the machine with Keras, in python for the CNN algorithm and would be exporting NumPy and panda for array classification. Conclusively after training the machines with human expressions, we would be successfully categorizing human emotions. Our project adjective is to design an emotion detection system from which machines can easily interpret human emotions. To feed human emotions, we have to train machines according to our needs, and for that, we would be using deep learning to teach the machines about human emotions. To cater to the emotion detection problem among machines, we would be building an emotion detection system that we will build on the framework of deep learning. We will train the machine with existent datasets of human emotions and will then compare it with real-time face detection. For this, we would be using Python as our basic language along with the CNN algorithm. The purpose of using CNN is its higher accuracy. The multi-layer algorithm refines the raw data to its meaningful form. CNN would be applied to real-time data that we would be collecting with the help of High Definition WebCam so that a clear picture can be captured. We would be using OpenCV software to map the face of real-time computer vision. The real-time data would then be compared with the pre-stored data set of human emotions. We would be taking 7 universally recognized human expressions i.e. happy, sad, angry, neutral, disgust, fear, and surprise. We would be then training the machine with Keras, in python for the CNN algorithm and would be exporting NumPy and panda for array classification. Conclusively after training the machines with human expressions, we would be successfully categorizing human emotions. Our project adjective is to design an emotion detection system from which machines can easily interpret human emotions. To feed human emotions, we have to train machines according to our needs, and for that, we would be using deep learning to teach the machines about human emotions.
Benefits of the Project- Machines can better understand human expressions and emotions while communicating, especially virtual AI chatbots, IoT devices and etc.
- It will help teachers in jotting live feedbacks of particular sessions from students in online learning sessions
- It will help doctors in monitoring patients expression during various mental therapies i.e. in EEG
- Can lower crimes by detecting their expressions and activity from CCTV cameras
The final deliverable will be in the form of web-based software in which the screen will be depicting the user's face, and a label will be highlighting their expressions.
Final Deliverable of the Project HW/SW integrated systemCore Industry ITOther Industries Education , Medical , Health Core Technology Artificial Intelligence(AI)Other Technologies Internet of Things (IoT), Augmented & Virtual Reality, RoboticsSustainable Development Goals Good Health and Well-Being for People, Quality Education, Sustainable Cities and CommunitiesRequired Resources| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 40600 | |||
| HD Camera and Cables | Equipment | 1 | 40000 | 40000 |
| Printing Documentations in Hard Copy | Miscellaneous | 30 | 20 | 600 |