🧑‍💻 Gender Detection using Deep Learning

📖 Abstract

Gender detection is a key aspect of human-computer interaction and surveillance systems. This project leverages deep learning and computer vision techniques to classify the gender of individuals from images and real-time webcam feeds. Using pre-trained convolutional neural networks (CNN) and Haar cascade face detection, the system achieves high accuracy and efficient performance.

🎯 Objectives

• Develop an efficient gender detection system using deep learning.
• Detect and extract faces from static images and video streams.
• Classify detected faces into Male or Female categories.
• Deploy a real-time gender detection application.

🛠️ Methodology

1. Face Detection
   • Uses OpenCV’s Haar Cascade and a pre-trained Caffe model (res10_300x300_ssd_iter_140000.caffemodel) to locate faces in images.
2. Gender Classification
   • CNN model trained on IMDB-WIKI dataset predicts gender from cropped face images.
3. Real-time Processing
   • Captures frames from webcam, detects faces, and overlays predictions on live video feed.

🖥️ Features

✅ Static image gender detection
✅ Real-time webcam gender detection
✅ High accuracy CNN classifier
✅ Lightweight model for fast inference
✅ Jupyter Notebook for demonstration and easy experimentation

image = cv2.imread('your_image.jpg') detect_and_predict_gender(image) run_webcam_detection()

📊 Results

📖 Full Code Overview

The notebook includes:

• 📂 Model Loading: Loads gender detection CNN and face detector.
• 🧠 Prediction Function: detect_and_predict_gender()
• 🎥 Webcam Stream: Captures frames and predicts gender in real-time.

🛠️ Technologies Used

📈 Future Enhancements

• 📱 Mobile App Deployment using TensorFlow Lite.
• 🌎 Multi-language support.
• 🔒 Privacy-Preserving Inference using Federated Learning.

Built With

• jupyter-notebook