A New Kernel Learning Algorithm for Wavelet Features-based Face Recognition
Zhou Xiao · 2007
A new kernel learning method called Kernel Nearest Neighbor Convex hull(KNNCH) algorithm is used for wavelet features based face recognition.Inspired by the intuitive geometric interpretation of SVM based on convex hulls,KNNCH maps the data in the original space to the kernel space with the kernel trick and constructs a nearest neighbor classifier in the kernel space,which takes the convex hulls of training sets as the extended class sets.The lower frequency features of face images extracted by 2D wavelet transform are efficient for face recognition.The features not only preserve the main information of face images,but also have the less dimensionality.KNNCH with wavelet features for face recognition shows very good performance,which can achieve 99.25% recognition rate with “leave-one-out” test method on ORL face database.