一种融合Hough变换和ASM定位瞳孔中心点方法

特征点的准确定位是机器视觉和模式识别的关键技术之一.主动形状模型(ASM)是一种传统的图像特征点定位算法,具有较高的精确性和鲁棒性.为了提高ASM人脸瞳孔特征点定位的精确度,提出了使用Hough变换方法来改进瞳孔特征点的定位.通过ASM算法,初步定位出瞳孔特征点,并使用Sobel算子对图像进行边缘检测,然后在人眼位置选择一个合适的窗口使用Hough圆检测,找出精确瞳孔点相对于ASM初步定位瞳孔点的偏移量.在实验室采集的人脸图像上的对比实验表明,该方法能够显著的改善ASM人眼瞳孔特征点定位准确性.由于使用了初定位进行了搜索范围的限制,计算量也得到了有效的控制.

A Method Combining Hough Transformation with ASM to Locate Pupil Landmarks

The problem of landmark location is one of the key technologies of machine vision and pattern recognition. Active shape models(ASM) is a traditional method of landmark location, which has high degree of accuracy and robustness in some case. To improve the pupil landmarks accuracy of face image, we propose a method which combines Hough Transformation with ASM. The positions of the pupil are initialized by ASM algorithm, and Sobel operator is used in edge detection and Hough circle detection around the eye position. Through these steps, it can calculate the offset of initial position. The experiments on the face image which were captured in our laboratory room show this method can obviously improve the accuracy of the pupil landmarks. With the limit of Initialization the positions, computation is reduced with our control.