Always release camera resources.

📊 Enhancing Accuracy

Improve detection by:

✅ Better lighting

✅ High-resolution webcam

✅ Adjusting scaleFactor

✅ Adjusting minNeighbors

🎨 Step 11: Build Streamlit App

Install: pip install streamlit

Upload Image:

import streamlit as st
uploaded_file = st.file_uploader(
    "Upload Image"
)

Detect Faces:

if uploaded_file:
    image = cv2.imread(
        uploaded_file.name
    )
    # detect faces
    st.image(image)

Users can upload photos and instantly detect faces.

⭐ Features to Add

Beginner

✅ Face Detection

✅ Image Upload

✅ Webcam Detection

Intermediate

✅ Face Counting

✅ Face Cropping

✅ Save Detected Faces

✅ Multiple Face Detection

Advanced

✅ Face Recognition

✅ Attendance System

✅ Emotion Detection

✅ Mask Detection

📂 Project Structure

face-detection-system/
│
├── data/
├── models/
├── screenshots/
├── app.py
├── detect.py
├── requirements.txt
├── README.md
└── sample_images/

💼 Resume Project Description

Face Detection System

Developed a real-time Face Detection System using Python and OpenCV. Implemented image preprocessing, Haar Cascade-based face detection, webcam integration, and an interactive application capable of detecting multiple faces in real time.

🎯 Mini Challenge

Upgrade your project by adding:

1. Face counting.

2. Face recognition using known images.

3. Attendance tracking.

4. Emotion detection.

5. Real-time Streamlit dashboard.

🔥 Double Tap ❤️ For Part-4