Gesture recognition is the process of interpreting human gestures via mathematical algorithms.
It involves the use of computer vision, machine learning, and artificial intelligence to identify and interpret human gestures.
There are several techniques used in gesture recognition:
Template matching involves comparing a gesture to a pre-defined set of templates to identify the closest match.
Statistical methods use probability distributions to identify the most likely gesture.
Hidden Markov models use a sequence of observations to identify the most likely gesture.
Neural networks use a set of interconnected nodes to identify patterns in the gesture.
Principal component analysis reduces the dimensionality of the gesture data to identify the most important features.
Support vector machines use a set of training data to identify the most likely gesture.
Gesture recognition has many applications, including in gaming, virtual reality, and healthcare.
As technology continues to advance, gesture recognition is becoming more accurate and reliable, making it an increasingly important tool in many industries.