基于思维进化算法的人脸特征点跟踪

Facial feature tracking based on mind evolutionary algorithm

提出了一种基于思维进化算法的人脸特征点跟踪的方法。通过提取人脸跟踪区域的尺度不变特征变换获得整体特征模版,并确定跟踪特征点,由整体特征模版的空间分布关系限定跟踪特征点的存在区域。应用思维进化算法的趋同过程和异化过程求得整体特征模版在目标帧的最优覆盖解,进一步提高跟踪特征点的限定区域的准确性,提高搜索速度和精度。引入原始特征和替代特征,使算法在复杂情况下仍能保持稳定跟踪和较快的跟踪速率。经试验,新方法能够在复杂情况下稳定跟踪95%的特征点,并保持25帧/s的跟踪速率。

A new facial feature point tracking method based on Mind Evolutionary Algorithm（MEA）was presented.The integrated feature model was formed by Scale Invariant Feature Transform（SIFT）feature in tracking region.Then the feature points were determined.The existing region of the feature points were restricted by the space distribution of the integrated feature model.The MEA was used to compute the best-case coverage solution by similartaxis procedure and dissimilation procedure in target frames.It can more precisely determine the restriction region of tracking feature points,and improve tracking precision and efficiency.The original feature and replaced feature were introduced to make the tracking steadier and faster in complicated condition.The experiment results indicate that the new method can stably track 95%feature points in complicated condition,while the tracking speed can be kept withing 25 frames per second.