Combining depth image and skeleton data from Kinect for recognizing words in the sign system for Indonesian language (SIBI [Sistem Isyarat Bahasa Indonesia])

The Sign System for Indonesian Language (SIBI) is a rather complex sign language. It has four components that distinguish the meaning of the sign language and it follows the syntax and the grammar of the Indonesian language. This paper proposes a model for recognizing the SIBI words by using Microsoft Kinect as the input sensor. This model is a part of automatic translation from SIBI to text. The features for each word are extracted from skeleton and color-depth data produced by Kinect. Skeleton data features indicate the angle between human joints and Cartesian axes. Color images are transformed to gray-scale and their features are extracted by using Discrete Cosine Transform (DCT) with Cross Correlation (CC) operation. The image´s depth features are extracted by running MATLAB regionprops function to get its region properties. The Generalized Learning Vector Quantization (GLVQ) and Random Forest (RF) training algorithm from WEKA data mining tools are used as the classifier of the model. Several experiments with different scenarios have shown that the highest accuracy (96,67%) is obtained by using 30 frames for skeleton combined with 20 frames for region properties image classified by Random Forest.