The Skk-means Algorithm And Its Application In Face Detection

Data Analysis is a significant problem in the Pattern Recognition and Machine Learning fields. A important meyhod of Data Analysis is the Clustering. It is the organization of a collection of patterns into clusters based on similarity. In order to reduce the error rate, the Semi-Supervised Learning (SSL) strategy and Kernel Method have been introduced into the clustering process.Face Detection (FD) is the process which searches faces in the input image, and gives its position, size and pose if there's any. It is a typical problem in PR fields. Nowadays, The study of FD algorithm based on skin color feature and clustering method attract more and more attention of researchers.This dissertation explore a semi-supervised clustering algorithm called Seed Kernel K-means (SKK-means) which is inspired by the kernel method and Seeding strategy based on the classical K-means algorithm. The algorithm uses a certain ratio of data points as the Seeds to generate initial cluster centers, and maps the data into feature space using kernel method. Our algorithm, which can be easily implemented, compares with respect to the other algorithm such as K-means and Kernel K-means, on 3 UCI databases (IRIS, Crabs and New-Thyroid) in some numeric experiment.This dissertation also raise a Skin Color Model (SCM) algorithm of FD based on SKK-means algorithm. The algorithm pretreated the input image firstly, and labels some pixels as the Seed points, then clustered each pixels of skin color as the Skin Color Clusters (SCC) using SKK-means algorithm. According to the probability of the pixels in SCC, we can obtained the SCM, and we can also obtain the skin regions after morphologic processing of the SCC. These skin regions can be used as the face candidate to put into the AdaBoost algorithm to detect human faces in practicality circumstance. Our experiments shows that it is more rapidly and accurately of our SCM algorithm to obtain the SCC. Thus, the Face Detection can be finally implemented after we got the SCM and face candidate.