Although water isn't as scarce in developed countries than LDC's, people in these developed countries, are always underestimate their drinking needs in their busy work environments. This project aims towards properly dispensing dihydrogen-monoxide to people who open their mouths and automate the water drinking process. In other words, we just wanted to shoot water into peoples mouth to keep them hydrated.
Our goal is to tackle SDG three, Good Health and Well Being, by creating an automate the water drinking system so people can focus on their everyday tasks while maintaining a healthy hydrated lifestyle.
Challenges We Faced
- How to detect facial movement as accurately as possible
- Finding a well-trained facial recognition dataset
- Sending post request to the hardware
It appears that we have built the finest automated ranged "dihydrogen-monxide" dispenser. We are proud that our product is semi-efficient in performing its tasks and could change direction based on facial position relative to the webcam.
What We've Learned
In this project, we explored the powerful foundation of OpenCV and it's facial recognition features. We learned to effectively maximize the dataset that we used to recognize faces and looked into better opportunities to optimize our code.
The Next Step
Second hand smoking is a very common yet detrimental activity in the world. It is the leading cause of lung cancer and asthma. Our next step aims towards training our model to recognize smokers and put out their cigarettes to help them quit smoking. In addition, our bot can traverse and effectively walk up to people and put out their cigarettes.