Robust Nose Detection and Tracking Using GentleBoost and Improved Lucas-Kanade Optical Flow Algorithms
Robust Nose Detection and Tracking Using GentleBoost and Improved Lucas-Kanade Optical Flow Algorithms
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DOI：
10.1007/978-3-540-74171-8_126
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发表时间：
2007-08
期刊：
影响因子：
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通讯作者：
X. Ren;Jiatao Song;H. Ying;Ya-ni Zhu;Xuena Qiu
中科院分区：
文献类型：
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作者：
X. Ren;Jiatao Song;H. Ying;Ya-ni Zhu;Xuena Qiu
人脸特征点检测问题是人脸图像分析、人机界面等领域的重要研究课题。本文提出了一种稳健的二维鼻检测与跟踪系统。该系统对于残疾人或手忙脚乱的情况很有价值。所需信息来自廉价的网络摄像头捕获的视频数据。使用基于Gabor小波特征的GentleBoost检测器来确定鼻尖的位置。在鼻尖初始定位后，使用改进的Lucas-Kanade光流法跟踪鼻尖特征点。实验表明，该系统能够以320×240像素的分辨率每秒处理18帧。这种方法将来将用于残疾用户的非接触式界面。
The problem of face feature points detection is an important research topic in many fields such as face image analysis and human-machine interface. In this paper, we propose a robust method of 2D nose detection and tracking system. This system can be valuable for disabled people or for cases where hands are busy with other tasks. The required information is derived from video data captured with an inexpensive web camera. Position of the nose tip is determined with the use of a Gabor wavelet feature based GentleBoost detector. Once the nose tip is initially located, an improved Lucas-Kanade optical flow method is used to track the nose tip feature point. Experiments show that our system is able to process 18 frames per second at a resolution of 320×240 pixels. This method will in future be used in a non-contact interface for disabled users.