Indian Sign Language gesture recognition using Discrete Wavelet Packet Transform

In recent days, Indian Sign Language (ISL) has been assumed to be more appealing gesture for speech and hearing impaired community. It helps us to understand the inherent meaning of this language for establishing a gesture based communicating system. In this paper, a novel hand gesture recognition technique has been introduced using Discrete Wavelet Packet Transform (DWPT). This technique provides more precise frequency resolution and more flexibility than DWT which helps to derive the invariant features. Dynamic hand gestures are collected in a constant background and variable light conditions. The DWPT technique has been applied on raw video data for data compression and eliminating unwanted noise. The Principal Component Analysis (PCA) has been used for dimensionality reduction and extracting the most significant features. The classification technique consists of different distance metrics and Artificial Neural Network (ANN) which demonstrates a comparative analysis of various types of classifiers. It has been observed from the experimental results that DWPT based technique performs better in comparison to Haar transform and wavelet transform.