Alert drowsy driver using OpenCV

As we all are aware of the fact, how deep learning and transfer learning is revolutionizing the world with its immense capability of handling any kind of data and learning so efficiently. So, similarly we have applied the same concept by picking a deep learning model i.e., Convolutional neural network which basically work son the principle of having filters. Each convolutional layer has some specific filters to identify and extract the features from the input image and learn it and transfer it to other layers for further processing. We can have as many filters as possible in the convolutional layer depending on the data we are dealing on. Filter are nothing but feature detectors in the input data. Along with the convolutional layer we also have other layers which does further pre-processing such as Maxpooling, Activation function, Batch Normalization and dropout layer. These all contribute to the CNN model creation and along with the flatten and output layer. The reason we do flattening is to feed the output of the CNN model to the dense layer which gives us the probability of the predicted value.

We used transfer learning to build our model. We used VGG-16 to achieve this feat. This is a very useful model to use for classification and has performed exceedingly well in the Imagenet competition. It takes input with the dimensions of 224,224,3.