Albanian Sign Language (AlbSL) Number Recognition from Both Hand's Gestures Acquired by Kinect Sensors

TL;DR

This paper presents a real-time system for recognizing Albanian Sign Language numbers using Kinect sensors, achieving 91% accuracy by analyzing hand gestures with Fourier descriptors and Euclidean distance.

Contribution

It introduces a novel real-time gesture recognition system for Albanian Sign Language numbers utilizing Kinect data and Fourier descriptors, with parallel processing for dual-hand recognition.

Findings

Achieves 91% recognition accuracy.

Processes 55 frames per second in real-time.

Effectively recognizes gestures from one or both hands.

Abstract

Albanian Sign Language (AlbSL) is relatively new and until now there doesn't exist a system that is able to recognize Albanian signs by using natural user interfaces (NUI). The aim of this paper is to present a real-time gesture recognition system that is able to automatically recognize number signs for Albanian Sign Language, captured from signer's both hands. Kinect device is used to obtain data streams. Every pixel generated from Kinect device contains depth data information which is used to construct a depth map. Hands segmentation process is performed by applying a threshold constant to depth map. In order to differentiate signer's hands a K-means clustering algorithm is applied to partition pixels into two groups corresponding to each signer's hands. Centroid distance function is calculated in each hand after extracting hand's contour pixels. Fourier descriptors, derived form centroid distance is used as a hand shape representation. For each number gesture there are 15 Fourier descriptors coefficients generated which represent uniquely that gesture. Every input data is compared against training data set by calculating Euclidean distance, using Fourier coefficients. Sign with the lowest Euclidean distance is considered as a match. The system is able to recognize number signs captured from one hand or both hands. When both signer's hands are used, some of the methodology processes are executed in parallel in order to improve the overall performance. The proposed system achieves an accuracy of 91% and is able to process 55 frames per second.