Recognizing human expressions and emotions has drawn the attention of researchers, as the capability of recognizing one?s expressions helps in human-computer interaction. It also helps in the right advertising campaigns and crowned with an augmented and enhanced human communication. Recent discoveri
EmotiBot: Human Emotions Recognizer Agent with Prototype Hardware
Recognizing human expressions and emotions has drawn the attention of researchers, as the capability of recognizing one’s expressions helps in human-computer interaction. It also helps in the right advertising campaigns and crowned with an augmented and enhanced human communication. Recent discoveries in neurosciences, together with the extension of notions like emotional intelligence and multilevel intelligence, has led to the emergence of the new framework “Affective Computing”, according to which, the main aim is to build machines that recognize, express, model, communicate and respond to user’s emotion indicators. In the new framework, emotions hold a key role in machines which could impact positively their future decisions, bringing closer to taking part in a more sociable loop of human-machine interaction. Our project implementation will be based upon the connection between Artificially Intelligent Agents and humans that will involve an emotional feedback framework, in which Artificially Intelligent Agents can understand emotions from some cues from human speech and facial expressions.
Our primary goal is to design an artificial intelligence program that will have the functionality to recognize human emotions through speech and facial expressions. Our secondary goal is to design a hardware architecture that will be able to provide all the above-described functionalities and provide the desired outcome. To achieve these goals, We must:

The user’s speech and his facial features are taken as an input. These features are then sent to the backend server where deep learning, NLP and image processing algorithms determine the emotions of the user. Appropriate response based on his/her emotions, for example if the user is angry the response could be something like “What brings us to tears, will lead us to grace. Our pain is never wasted”. This response is then provided as speech by our agent.
Software:
On the software side our project consists of multiple deep learning models trained for recognzing human emotions through speech, text and facial features and another deep learning model that responds to the user based on his/her emotions and current cognitive state.
Hardware:
On the hardware side our project consists of a small robotic prototype (much like in the movie Wall - E) which listens to a user's speech, captures his/her facial features and responds to the user via a speaker and displays facial features on a screen.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Raspberry Pi 4 | Equipment | 1 | 15000 | 15000 |
| 7 inch screen | Equipment | 1 | 9000 | 9000 |
| USB Microphone | Equipment | 1 | 9000 | 9000 |
| Speaker | Equipment | 1 | 5000 | 5000 |
| Camera | Equipment | 1 | 5000 | 5000 |
| Total in (Rs) | 43000 |
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