Hand Gesture Classification using Deep Learning with Keras

In recent years, Deep Learning has become extremely popular, being the part of Machine Learning, it has several applications in today’s world from classifying pictures and translating languages to putting together a self-driving car. Today all these tasks are being controlled by computers itself with none human intervention.

 

 

Why Deep Learning?

 

In this “Big Data Era” of technology, we have a lot of data, which is not possible to process using traditional machine learning algorithms but possible with deep learning. It is obvious that if the data increase then deep learning will certainly increase the performance of the model. So Deep Learning is gaining much popularity due to its supremacy in terms of accuracy once trained with a vast amount of data.

 

Generally, deep learning is a machine learning algorithm in which we feed an input X and use it to predict an output Y like given some labeled images of cats and dogs and then use deep learning to make predictions for the new unlabelled image of cat or dog. Now the question arises is how deep learning does this? So basically deep learning algorithm is made up of a neural network that takes a labeled dataset and tries to find out the pattern between input and its labels like the human brain does and then based on this experience it makes predictions.

 

It consists of an input layer, hidden layers and output layer which is made up of a collection of neurons (or nodes). Input layers take an array of numbers(i.e. In case of images -pixel values), predictions at the output layer, while hidden layers are correlated with most of the computation. When there is a case of a large number of neural layers in any network then it will be deep neural network and then only deep learning comes into existence, thus deep neural network improves its ability to approximate additional complex functions.

 

 

Types of Neural Network

 

There exist various types of neural networks for different tasks like Dense Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks and many more. Apart from these layers, there exist other layers which include Pooling Layers, Dropout Layers, Activation Functions, etc, all these added up to make the outcomes of model efficient.

 

 

Convolutional Neural Network (CNN)

 

CNN is mainly used for computer vision which includes image classification, face recognition, object detection, etc. CNN, for image classification, followed by neural layers and activation functions takes an input image and treats it as an array of pixels, processes it by finding patterns in the image and then classifies it into certain categories or classes given in the problem statement. Image( the input layer) accepts will be either an array of RGB matrix ( 3 channels) or grayscale matrix (1 channel).

 

 

CNN Architecture

 

In deep learning, the CNN architecture will be composed of convolutional layers(filters), Pooling layers, fully connected layers (Dense) and then output layers.

 

 

Convolutional Layer 

 

In this, the filter will be used to learn features from the image by convolving it with image, a mathematical operation, and then a new matrix will form i.e. with the learned features like edge detections, sharpen or blurred image.

 

Matrix visualization in a convolutional neural network

 

 

Pooling Layer

 

This layer would be used to perform downsampling operation on the input of this layer so that the input image will be compressed i.e. from a more detailed image to a less detailed image with more information. It can be of different types: 

 

1. Max Pooling

2. Average Pooling

3. Sum Pooling

 

Pooling layer in CNN architecture

 

Max Pooling takes the largest element from the rectified feature map and Sum Pooling takes the sum of all elements in the feature map.

 

 

Fully Connected Layer

 

At this layer, just after the CNN network followed by pooling, the matrix will be flattened before feeding to Fully Connected Layer like a neural network. Then at the final layer, there will be activation function softmax or sigmoid to classify the output with the no of neurons in the final layer equals to no of classes in the output.

 

Thread of fully connected layer between input and output data 

 

 

Activation Function

 

It is a nonlinear layer put at the end of or in between neural networks which then decide the output of that node. Available activation functions are: Sigmoid, ReLU, Leaky ReLU, Softmax etc.

 

List of various Activation Function

 

 

Hand Gesture Classification using Python Code

 

In the Hand Gesture Classification, we used a dataset that contains images of different hand gestures, such as a fist, palm, showing the thumb, and others which can be further useful to show counts from 1 to 5 with these hand gestures. 

 

There are many algorithms in machine learning for classification out of which we'll be using Deep learning with the help of Convolution Neural Network (CNN) as discussed above, with the help of Keras ( an open-source neural network library written in Python). Keras is easy and fast and also provides support for CNN and runs seamlessly on both CPU and GPU.

 

Technologies Used:

1. Python3

2. Tools & Libraries used:

   1. Google Collaboratory

   2. Keras

   3. TensorFlow

 

 

Import all the required libraries