Xibin Jia
Beijing University of Technology

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Indonesian Journal of Electrical Engineering and Computer Science

A Kind of Visual Speech Feature with the Geometric and Local Inner Texture Description Xibin Jia; Yanfeng Sun
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: February 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In this paper, we propose a type of joint feature with geometric parameters and color moments to represent the speaking-mouth frames for image-based visual speech synthesis systems. Based on FDP around the mouth area, the geometric feature is obtained by computing Euclidean distances to describe the width of the speaking mouth, the height of the outer and inner lips and the distances between them. The color moment component in the joint feature is obtained by calculating the texture between the upper and lower inner lips to describe the visibility state of the teeth. Through analyzing the accordance between the teeth visibility and the components of RGB and HSV color space based on the samples separately, we discovered that green and blue components are good at describing the change of teeth visibility. The experiments show that the proposed joint feature can effectively provide the basis for categorizing the different speaking states especially at the sense of lip shapes and tooth visibility. The evaluation of clustering results is done by analyzing the derived parameters of the silhouette function.  The analyzing results prove that comparing with the geometric only and PCA, our proposed feature together with the shape and the local inner lip texture clues has better performance in improving the similarity between samples within the clusters. In the future, more expressive features with the shape and local texture information should be explored to increase the proportion of similar samples within the clusters to improve the descriptive ability of speaking mouths. DOI: http://dx.doi.org/10.11591/telkomnika.v11i2.2047