This paper presents appearance based methods for face recognition using linear and nonlinear techniques. The linear algorithms used are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The two nonlinear methods used are the Kernel Principal Components Analysis (KPCA) and Kernel Fisher Analysis (KFA). The linear dimensional reduction projection methods encode pattern information based on second order dependencies. The nonlinear methods are used to handle relationships among three or more pixels. In the final stage, Mahalinobis Cosine (MAHCOS) metric is used to define the similarity measure between two images. The experiment showed that LDA and KFA have the highest performance of 93.33 % from the CMC and ROC results when used with Gabor wavelets. The overall result using 400 images of AT&T database showed that the performance of the linear and nonlinear algorithms can be affected by the number of classes of the images, preprocessing of images, and the number of face images of the test sets used for recognition.

Abstract;Security of networking systems has been an issue since computer networl

The NER task can be considered solved for English and a few other European languages given the available research outputs, tools, resources and applications involving NER for these languages. The scenario is sharply different for Nigerian and most of African languages and hence the motivation for the research reported in this paper. The paper presents an exploration of the potency of some language independent features in the recognition of the mentions of persons, locations and organizations in Yorùbá text in a supervised machine learning setup. The results are promising but as further investigations revealed, the size of the training corpus is yet an issue that needs to be addressed.