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6.6.0
title: Face Counter & Density Estimator
emoji: 🎯
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 5.29.0
app_file: app.py
pinned: false
🎯 Project Title: Face Counting and Crowd Density Estimation using MTCNN 🔍 Overview This project addresses the problem of estimating the number of faces in an image, with the added goal of classifying crowd density as Sparse, Medium, or Dense. Initially approached as a regression task, we shifted strategies after empirical evaluation showed that using ground-truth bounding box annotations from the WIDER FACE dataset provided more reliable face counts than a trained regressor. We ultimately adopted the MTCNN (Multi-task Cascaded Convolutional Networks) approach for real-time face detection due to its robustness, accuracy, and speed in varying crowd scenarios.
🧠 Why MTCNN? MTCNN is a popular face detection framework because:
It combines face detection and facial landmark localization, making it suitable for fine-grained analysis.
It performs well across scales, poses, and lighting conditions.
It runs in real-time, enabling its use in live applications like webcam feeds.
It's pretrained and optimized, saving time and training resources.
💡 How It Works A user uploads an image or uses their webcam to capture one.
The app runs MTCNN to detect faces and count bounding boxes.
Based on the count:
Sparse: 1–10 faces
Medium: 11–50 faces
Dense: 51+ faces
Results are overlaid directly on the image, offering a visual interpretation of the density.
🛠️ Tech Stack Model: facenet-pytorch MTCNN
Dataset: WIDER FACE (for validation and benchmarking)
Framework: Python, Streamlit
Libraries: OpenCV, PIL, Torch, Matplotlib
Deployment: Streamlit (local or cloud-hosted)
🌍 Real-Life Applications Surveillance & Public Safety Detect unusually dense crowds in public areas to trigger alerts.
Event Management Monitor real-time foot traffic and optimize crowd control in concerts, rallies, etc.
Retail Analytics Gauge customer distribution across zones in malls or stores.
Transportation Hubs Analyze crowd density in airports or stations to deploy personnel dynamically.
Smart Cities Integrated with CCTV systems, the app can be part of intelligent urban monitoring.
Pandemic Safety Enforcement Identify when crowd limits are exceeded to enforce health protocols.
✅ Impact This project demonstrates how a lightweight, pretrained architecture like MTCNN can replace heavyweight regression-based models when data annotation is reliable. It bridges computer vision with social and safety applications, showing how academic tools can translate into real-world solutions.