Facial Recognition System for Retail Spaces

Project Goals

Our Solution

From the start, we prioritized accuracy and processing speed, as the system needed to analyze video streams in real time. We developed a dedicated module where a neural network trained using Python-based computer vision libraries (OpenCV, PyTorch) processes frames and identifies faces. Additionally, we implemented a C++ component to optimize computational performance, ensuring stable operation even with a high volume of visitors. To enhance functionality, we integrated an analytics system that evaluates demographic data (age groups, gender) and visitor dwell time. This provides shopping center management with valuable insights for operational planning. For ease of use, all data is visualized in a web dashboard built with React, enabling both security teams and marketing departments to: respond quickly to security incidents, optimize staff scheduling based on peak visitor hours, plan promotions and events based on real-time visitor trends.

Technologies Used

Results

Project Team

Implementation Timeline

4 months

Project Uniqueness

This system combines high-accuracy facial recognition with real-time visitor analytics, providing both security and marketing value (traffic flow insights).

Estimated Cost

From $15,000