提出了一种基于分形编码图形表示的人脸识别改进算法。该算法在分形图像压缩概念的基础上，定义图像中的像素块（值域块）作为基元。首先计算人脸图像的分形压缩编码，以此为基础寻找像素块间的内在联系，生成图像的图形表示——带循环植物；然后利用带循环植物获得每个像素块最终收敛时的仿射变换参数；最后通过定义合适的距离度量来进行人脸识别。该算法与基于像素的图形表示方法相比，识别率高、识别速度快、鲁棒性好。此外，还对一般情况下带循环植物的生成过程进行了深入的研究和讨论，扩展和丰富了前人研究的成果。

In this paper，a fractal based graph theoretic approach for face recognition is presented.Based on the image compression of fractal,pixel blocks(Range blocks) are defined as basal elements. The interdependence of pixel blocks is inherent within the fractal code in the form of circular plants. Fristly，the fractal code of face images is calculated and the corresponding circular plants can be obtained.Secondly，affine parameters for each pixel block are computed according to the acquired circular plants. Lastly，face recognition can be realized by appropriate distance measurement. The presented way is more effective，faster and more robust than the previous technique based on pixels. In addition，we detail the growth of circular plants in a general case to cover the shortage of predecessor.