This repository contains code for a Convolutional Neural Network (CNN) classification model designed to predict whether a person is sleeping or not based on the detection of closed eyes. The model also takes into account yawning as an additional feature for sleep detection.
The model has been trained on a dataset consisting of images with labels indicating whether the person in the image is sleeping or not. The dataset includes variations in lighting conditions, head poses, and facial expressions.
The CNN model architecture is as follows: model = Sequential()
model.add(Conv2D(16, (3, 3), 1, activation='relu', input_shape=(256, 256, 3))) model.add(MaxPooling2D()) model.add(Conv2D(32, (3, 3), 1, activation='relu')) model.add(MaxPooling2D()) model.add(Conv2D(16, (3, 3), 1, activation='relu')) model.add(MaxPooling2D()) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dense(1, activation='sigmoid'))
This architecture consists of convolutional layers for feature extraction, max-pooling layers for down-sampling, and fully connected layers for classification. The final layer uses the sigmoid activation function for binary classification.
The dataset was split into training, validation, and test sets with the following proportions: 70% training, 20% validation, and 10% test. The model was trained using binary cross-entropy loss and the Adam optimizer.
https://www.kaggle.com/datasets/dheerajperumandla/drowsiness-dataset?select=train
