Survey on Various Gesture Recognition Techniques for Interfacing Machines Based on Ambient Intelligence

TL;DR

This survey reviews various gesture recognition techniques for machine interfacing in ambient intelligence, comparing data-glove and vision-based approaches, and discussing facial feature detection methods like nose and eye detection.

Contribution

It provides a comprehensive comparison of gesture recognition systems and facial feature detection methods for ambient intelligence interfaces, highlighting their advantages and limitations.

Findings

Data-glove and vision-based approaches are the main categories of gesture recognition.

Facial features like nose tip and eye detection are crucial for face-based gesture analysis.

Traditional eye detection methods include template, appearance, and feature-based techniques.

Abstract

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.