scalable
Face recognition system for retail
Summary
As demand grows, retail shops struggle to create the best customer service, since seldom do they completely understand a regular person's needs. This is why our solution provides:
- Insight into the customer opinion of the provided service through emotion recognition.
- Security for customers and workers through criminal detection based on a provided database of dangerous individuals with the ability to manually update it.
- Comprehensive customer analysis including gender and age.
- FaceID-like system for keeping track of regular customers.
7 months
Project length
5 people
3 deep learning engineers, 1 designer, 1 frontend developer and 1 backend developer.
Technologies
- Python
- OpenCV
- Pytorch
- FaceNet
- aiohttp
- React
- nextjs
- OpenCV
- Pytorch
- FaceNet
- aiohttp
- React
- nextjs
Tech Challenge
- Real-time performance with low computational power.
- Sustainable data storage with fast I/O.
- Easy-to-deploy solution.
Solution
- Our solution applies fine-tuned and optimized FaceNet for recognition and customer analysis.
- For the criminal detection, we use embeddings creation with similarity indexes.
