Hand Gesture Recognition Based on Karhunen-Loeve Transform

TL;DR

This paper presents a hand gesture recognition system utilizing the Karhunen-Loeve Transform, achieving a 96% recognition rate through a multi-step process including skin filtering, palm cropping, edge detection, feature extraction, and classification.

Contribution

The paper introduces a novel hand gesture recognition method based on K-L Transform with a complete system pipeline and high accuracy, demonstrating its effectiveness.

Findings

Achieved 96% recognition accuracy for 10 gestures

Developed a five-step recognition system including skin filtering and feature extraction

Validated the approach's simplicity and effectiveness

Abstract

In this paper, we have proposed a system based on K-L Transform to recognize different hand gestures. The system consists of five steps: skin filtering, palm cropping, edge detection, feature extraction, and classification. Firstly the hand is detected using skin filtering and palm cropping was performed to extract out only the palm portion of the hand. The extracted image was then processed using the Canny Edge Detection technique to extract the outline images of palm. After palm extraction, the features of hand were extracted using K-L Transform technique and finally the input gesture was recognized using proper classifier. In our system, we have tested for 10 different hand gestures, and recognizing rate obtained was 96%. Hence we propose an easy approach to recognize different hand gestures.