Lip segmentation using automatic selected initial contours based on localized active contour model

被引:4
|
作者
Lu, Yuanyao [1 ]
Liu, Qingqing [1 ]
机构
[1] North China Univ Technol, Sch Elect & Informat Engn, 5 Jinyuanzhuang Rd, Beijing 100144, Peoples R China
基金
中国国家自然科学基金; 北京市自然科学基金;
关键词
Lip segmentation; Localized active contour model; Initial contour; Illumination equalization; Color space; IMAGE SEGMENTATION; EXTRACTION; SNAKES;
D O I
10.1186/s13640-017-0243-9
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
With the rapid development of artificial intelligence and the increasing popularity of smart devices, human-computer interaction technology has become a multimedia and multimode technology from being computer-focused to people-centered. Among all ways of human-computer interactions, using language to interact with machines is the most convenient and efficient one. However, the performance of audio speech recognition systems is not satisfied in a noisy environment. Thus, more and more researchers focus their works on visual lip reading technology. By extracting lip movement features of speakers rather than audio features, visual lip reading systems can get superior results when noises and interferences exist. Lip segmentation plays an important role in a visual lip reading system, since the segmentation result is crucial to the final recognition accuracy. In this paper, we propose a localized active contour model-based method using two initial contours in a combined color space. We apply illumination equalization to original RGB images to decrease the interference of uneven illumination. A combined color space consists of the U component in CIE-LUV color space and the sum of C2 and C3 components of the image after discrete Hartley transform. We select a rhombus as the initial contour of a closed mouth, because it has a similar shape to a closed lip. For an open mouth, we utilize a combined semi-ellipse as the initial contours of both outer and inner lip boundaries. After attaining the results of each color component separately, we merge them together to obtain the final segmentation result. From the experiment, we can conclude that this method can get better segmentation results compared with the method using a circle as the initial contour to segment gray images and images in combined color space, especially for open mouth. An extremely obvious advantage of this method is the results of open mouth excluding internal information of mouth such as teeth, black holes, and tongue, because of the introduction of the inner initial contour.
引用
收藏
页数:12
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