Real-Time Hand Gesture Recognition Using the Leap Motion Controller and Machine Learning

Currently, gesture recognition is presented as a problem of feature extraction and pattern recognition, in which we label a movement as belonging to a given class. The response of a gesture recognition system can be applied for different problems in different fields, such as medicine, teaching, robotics among others. There are different models proposed in the scientific literature that try to solve the problem of hand gesture recognition. These works do not meet the demand for real-time processing and high recognition accuracy, simultaneously. In this context, the present work describes a project to develop a new model for the recognition of hand gestures using infrared information, acquired with the Leap Motion Controller, and also using machine learning techniques. The proposed model is intended to recognize 5 static and 4 dynamic gestures of the hand in real-time and with high accuracy, simultaneously. The methodology that will be used for developing this work is composed of two phases, the training and testing: In the first phase, we will design, classify, preliminary validation and tune the proposed model. In the second phase, we will test the proposed model by estimating its accuracy of recognition and time of processing.