Hand Gesture Recognition with Python

In this article, I will take you through a very simple Machine Learning project on Hand Gesture Recognition with Python programming language. Hand gesture recognition system received great attention in the recent few years because of its manifoldness applications and the ability to interact with machine efficiently through human-computer interaction.

Hand Gesture Recognition Model

The essential objective of building a hand gesture recognition model is to create a natural interaction between human and computer where the recognized gestures can be used to control a robot or transmit meaningful information.

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The gestures can be static (posture or certain pose) which require less computational complexity or dynamic (sequence of postures) which are more complex but adapted to real-time environments. In this article, I will train a very simple model that can be easily understood by machine learning newbies.

The hand gesture recognition system has been applied for different applications in different fields including; translation into sign language, virtual environments, intelligent monitoring, robot control, medical systems, etc.

Machine Learning Project on Hand Gesture Recognition Model

Now let’s see how to train a Machine Learning model in Hand Gesture Recognition with Python programming language. I will start with importing the necessary libraries and reading the datasets that we need for this task:

Now I will split the data into 75% training and 25% test set:

Now I will rescale the data using Standard Scalar:

Now I will use the Random Forest Classifier to train a Hand Gesture Recognition model with Python:

Now let’s check the accuracy of the model using the confusion matrix and print the classification report of our machine learning model:

from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix

print('Classification Report: \n', classification_report(y_test,y_pred))
print('Confusion Matrix: \n', confusion_matrix(y_test,y_pred))

Classification Report: 
               precision    recall  f1-score   support

           0       0.94      0.97      0.95       719
           1       0.96      0.92      0.94       769
           2       0.91      0.95      0.93       703
           3       0.89      0.86      0.87       729

    accuracy                           0.92      2920
   macro avg       0.92      0.93      0.92      2920
weighted avg       0.92      0.92      0.92      2920

Confusion Matrix: 
 [[698   0   7  14]
 [  0 709  22  38]
 [  4   7 665  27]
 [ 43  25  33 628]]

I hope you liked this article on Hand Gesture Recognition model using the Python programming language. Feel free to ask your valuable questions in the comments section below.