Facial Recognition & Machine Learning
To identify the customers who walked into a cafe/restaurant
The client was looking to put together a demo of a product. It would be a camera at a cash register that could identify people as they walked into a cafe/restaurant. The camera could identify people as they stood in a line at the register. The camera would then report to a tablet.
They had a development team to take care of the display on the tablet and other aspects of the same. They needed to own the code after we were completed. They didn’t have the expertise of facial recognition so this was why they connected with us.
That was a for a demo but there could be more work going forward.
How we addressed the situation
- We built a “people counter” with OpenCV and Python.
- Using OpenCV, we counted the number of people who are heading “in” or “out” of a department store in real-time.
- We classified images with deep learning and OpenCV 3.3’s deep neural network ( dnn ) module.
- Object detection was programed using deep learning and Single Shot Detectors and MobileNets.
- We combined both the MobileNet architecture and the Single Shot Detector (SSD) framework, to create an efficient deep learning-based method to process the object detection.
- Lab test