基于Mean shift算法的灰度人脸跟踪

Algorithm for Gray Face Tracking Based on Mean Shift

由于灰度图像的信息单一、缺乏描述目标的信息,且易受到光照的影响,导致Meanshift算法在灰度图像跟踪中的应用不稳定,我们采用结合灰度与方向编码特征建立目标模型的策略,提出了在灰度图像中以Meanshift为核心的人脸跟踪算法。实验结果表明,该算法可以成功的克服混乱、遮挡、光照变化以及目标自身的缩放与旋转的影响,并且计算量非常小,对于大小为48像素×68像素的目标,整个算法的计算时间仅为34ms。

In gray-level image sequence, due to the sensitivity to illumination and the lack of the information for the object representation, the face tracking based on the mean shift algorithm may be unstable. In order to apply mean shift algorithm in gray-level image effectively, a strategy which integrates intensity and orientation codes to represent target is adopted. A new algorithm is presented for face tracking in this paper. Experiment results show that the new algorithm can successfully cope with clutter, partial occlusions, illumination change and target variations such as scale and rotation. The computational complexity is greatly reduced. If the size of the target is 48×68 pixels, the computation time of the whole algorithm is about 34ms.