一种改进的CAMShift跟踪算法及人脸检测框架

为充分利用人脸视频图像序列中的时空信息,获得更加准确的人脸比对图像序列,提出一种结合人脸跟踪的人脸检测框架。使用简单快速的正面人脸检测算法对人脸视频图像序列进行检测,用检测的结果对人脸跟踪算法进行初始化及校验和调整。为解决CAMShift跟踪算法容易受类肤色区域影响而导致提取到的人脸区域存在冗余信息的问题,提出一种改进的CAMShift-KLT算法。该算法利用兴趣点跟踪人脸图像的边缘,达到准确获取人脸比对图像的目的。实验结果表明,与CAMShift算法相比,CAMShift-KLT算法获取的人脸区域更精准,同时具有较小的跟踪偏移距离、较大的跟踪命中率和更高的跟踪有效性。与对比算法相比,CAMShift-KLT算法能够获得与理想的人脸区域更加一致的跟踪区域。

An improved CAMShift tracking algorithm anda face detection framework

To make full use of human face videos in temporal and spatial domains and obtain accurate face image sequence for face alignment, we propose a face detection framework in combination with a face tracking algorithm. We use the simple and fast frontal face detection algorithm to detect face images from videos, and then employ the detection results to initialize, check and adjust the face tracking results. However, the CAMShift tracking algorithm is vulnerable to the negative impact of skin color areas, which leads to more redundant information. To solve this problem, we propose an improved CAMShift-KLT algorithm, which utilizes interest points to trace the edge of the face image and acquires accurate face alignment images. Experimental results show that compared with the CAMShift algorithm, the proposed algorithm can obtain more accurate face regions, and meanwhile, it has smaller tracking migration distance, higher hit rate of tracking and tracking effectiveness. Besides, compared with the contrast algorithm, the improved CAMShift-KLT algorithm can better track the face region.