Jun 23, 2022
No image
Completed
Image Recognition Service
$50,000+
4-6 months
Japan
6-9
Service categories
Service Lines
Mobile Development
Domain focus
Other
Challenge
In Japan, many people send their children between the ages of 0 and 5 to preschool, where kids stay on weekdays from morning until 3-6 pm. The conditions for children in preschool institutions are monitored through regular reporting established by the institutions.
The goal of the project was to help educators to use face recognition technologies in the following:
► Simplify the interaction of educators with the recipients (parents) of the child depicted in the photo.
► Display the previous number of photos and the photos of each child sent previously.
► During the shooting, mark the children whose photo it is forbidden to take.
In Japan, many people send their children between the ages of 0 and 5 to preschool, where kids stay on weekdays from morning until 3-6 pm. The conditions for children in preschool institutions are monitored through regular reporting established by the institutions.
The goal of the project was to help educators to use face recognition technologies in the following:
► Simplify the interaction of educators with the recipients (parents) of the child depicted in the photo.
► Display the previous number of photos and the photos of each child sent previously.
► During the shooting, mark the children whose photo it is forbidden to take.
Solution
CHI Software development team was involved from the very beginning. We conducted research, tried, and tested more than 10 neural networks for obtaining landmarks and face embeddings. Most of them had limitations that didn't allow us to run them locally on the device. But at the end of the day, we succeeded.
We solved the following issues:
► Registration of children (the ability to take a photo of one child and save it to the database on the device)
► Processing of the faces in the photo in real-time
► Comparing the detected face images with the face images in the database and labeling the found faces
► We had limitations on the accuracy of the F1 measure not lower than 85% and the speed of the system not exceeding 3/4 of a second
CHI Software development team was involved from the very beginning. We conducted research, tried, and tested more than 10 neural networks for obtaining landmarks and face embeddings. Most of them had limitations that didn't allow us to run them locally on the device. But at the end of the day, we succeeded.
We solved the following issues:
► Registration of children (the ability to take a photo of one child and save it to the database on the device)
► Processing of the faces in the photo in real-time
► Comparing the detected face images with the face images in the database and labeling the found faces
► We had limitations on the accuracy of the F1 measure not lower than 85% and the speed of the system not exceeding 3/4 of a second
Results
► In the POC phase, we developed functionality for registering children and searching for faces in real-time.
► We managed to achieve the processing and recognition of 5 faces at the same time and the speed of 3/4 second in real-time.
► In the POC phase, we developed functionality for registering children and searching for faces in real-time.
► We managed to achieve the processing and recognition of 5 faces at the same time and the speed of 3/4 second in real-time.