Consideration of a Selecting Frame of Finger-Spelled Words from Backhand View

Abstract

To understand finger alphabet from backhand sign video, there are many redundant video frames between consecutive alphabets and among video frames of an alphabet. These redundant video frames cause loss in finger alphabet understanding, and should be considered to delete. This paper proposes a method to select significant video frames of sign for finger-spelled words of each letter to make more information from backhand view. In this method, finger-spelled words video is divided into frames, and each frame is converted to a binary image by an automatic threshold, and a binary image change to contour frames. Then, we apply the located centroid as the center of the contour image frame to calculate the distance to all boundaries of image frames. After that, all distances of each frame are presented as signature signals that identify each frame, and these values are used with the selected frame equation to select a significant frame. Finally, 1D Signature signal as their feature is extracted from selected frames. For evaluation of our proposed method, 6 samples of finger-spelled words of the American Sign Language (ASL) are used to select a significant frame, and Hidden Markov Models (HMM) is used to classify the words. The accuracy of the proposed method is evaluated 97.5% approximately.