Gesture recognition is mainly apprehensive on analyzing the functionality of human wits. The main goal of
gesture recognition is to create a system which can recognize specific human gestures and use them to
convey information or for device control. Hand gestures provide a separate complementary modality to
speech for expressing ones ideas. Information associated with hand gestures in a conversation is degree,
discourse structure, spatial and temporal structure. The approaches present can be mainly divided into
Data-Glove Based and Vision Based approaches. An important face feature point is the nose tip. Since
nose is the highest protruding point from the face. Besides that, it is not affected by facial expressions.
Another important function of the nose is that it is able to indicate the head pose. Knowledge of the nose
location will enable us to align an unknown 3D face with those in a face database.
Eye detection is divided into eye position detection and eye contour detection. Existing works in eye
detection can be classified into two major categories: traditional image-based passive approaches and the
active IR based approaches. The former uses intensity and shape of eyes for detection and the latter works
on the assumption that eyes have a reflection under near IR illumination and produce bright/dark pupil
effect. The traditional methods can be broadly classified into three categories: template based methods,
appearance based methods and feature based methods. The purpose of this paper is to compare various
human Gesture recognition systems for interfacing machines directly to human wits without any corporeal
media in an ambient environment.