Face Mask Detection
Project Title : Face Mask Detection
Objective:
To create a machine learning system that can detect whether a person is wearing a face mask or not in real-time using computer vision.
Technologies Used:
Programming Language: Python
Libraries/Tools: OpenCV, TensorFlow/Keras, NumPy, Matplotlib
Frameworks: CNN (Convolutional Neural Network)
Dataset: Labeled dataset of images with and without face masks (e.g., Kaggle or custom dataset)
Approach:
Data Collection & Preprocessing:
Gather images of people with and without masks
Resize images, convert to grayscale or RGB
Normalize pixel values and split into train/test sets
Model Building:
Use a CNN architecture to learn facial features
Layers include: Convolution, MaxPooling, Flatten, Dense (Fully Connected), and Softmax (for classification)
Optionally use Transfer Learning (e.g., MobileNetV2) for better results and faster training
Training & Evaluation:
Train the model to classify images into “Mask” and “No Mask” categories
Evaluate using accuracy, precision, recall, and confusion matrix
Real-Time Detection (Optional):
Use OpenCV with the webcam to detect faces
Apply the trained model to predict mask status in real-time
Outcome:
A functional machine learning system that can accurately detect face masks in images or live video, useful for safety monitoring during health crises like COVID-19.
₹ 999 /-
Project includes:
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Customization
Fully
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Security
High
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Performance
Fast
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Future Updates
Free
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Total Buyers
500+
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Support
Lifetime
Secure Payment:
