  Nowadays actions are increasingly being handled in electronic ways, instead
of physical interaction. From earlier times biometrics is used in the
authentication of a person. It recognizes a person by using a human trait
associated with it like eyes (by calculating the distance between the eyes) and
using hand gestures, fingerprint detection, face detection etc. Advantages of
using these traits for identification are that they uniquely identify a person
and cannot be forgotten or lost. These are unique features of a human being
which are being used widely to make the human life simpler. Hand gesture
recognition system is a powerful tool that supports efficient interaction
between the user and the computer. The main moto of hand gesture recognition
research is to create a system which can recognise specific hand gestures and
use them to convey useful information for device control. This paper presents
an experimental study over the feasibility of principal component analysis in
hand gesture recognition system. PCA is a powerful tool for analyzing data. The
primary goal of PCA is dimensionality reduction. Frames are extracted from the
Sheffield KInect Gesture (SKIG) dataset. The implementation is done by creating
a training set and then training the recognizer. It uses Eigen space by
processing the eigenvalues and eigenvectors of the images in training set.
Euclidean distance with the threshold value is used as similarity metric to
recognize the gestures. The experimental results show that PCA is feasible to
be used for hand gesture recognition system.
