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This study explored identity mobilization and conflict among oil producing communities in the Niger Delta in Nigeria with an analysis of conflict involving Eket and Ibeno people in Akwa Ibom State. Fashioned after the survey tradition, primary data were obtained from key informant interviews while published materials provided secondary data. Inspired by the social disorganization theory, findings show how increasing recognition of the accumulative potentials of oil engendered violent conflict between the two ethnic groups as they struggled for greater access to oil benefits. Youths carried out the violent clashes, while local elites fueled the conflict by providing political coverage and arms. Identity mobilization through discourses of autochthony and locality played a central role in the conflict, as each group was organized in order to enhance competitive efficiency. The conflict shows the centrality of oil in Nigeria's political economy, and how it shapes both national political discourse and the broad rhythms of accumulation, rent-seeking disposition and social conflict, including conflict among oil producing communities.

This paper proposes the use of redundant features for efficient recognition of faces in still images using a novel system framework that offers a detailed systematic workflow for solving the facial recognition problem. It accepts still frontal face images, processes and represents salient facial features (face, eyes, nose, and mouth) using facial detection and extraction techniques. The extracted features are then modeled by an ensemble of self-organizing maps. The ensemble outputs are later reassembled into a single dataset consisting of a normalized image Euclidean distance matrix to enhance the search space for optimal convergence and classification. The feasibility of the framework is tested using an experiment facial database captured during the study, and three benchmark facial expression databases, namely the Extended Cohn-Kanade (CK+) database, the Japanese Female Facial Expressions database, and the MMI Facial Expression database. The results suggest that feature redundancy is indeed useful for efficient facial recognition, as the support vector machine classification recorded high accuracies across the various databases, with the normalized image Euclidean distance dataset producing the highest performance, when compared with the localized principal component analysis and unnormalized image Euclidean distance datasets. Furthermore, overall classification accuracy of above 99% was achieved for the experiment (nonexpressive still face) database, compared with the benchmark facial expression databases, which yielded slightly lower results. A future direction of this work is further improvement of the framework to robustly handle severe facial variations.