There are different parameters which degrade the performance of a face biometric system due to their variations. The baseline biometric systems can get relief to some extent from this kind of negative effect by utilizing the information of the cohort images and fusion methods. But to achieve the set of suitable cohorts for each and every enrolled person is a task of great challenge. Determining the cohort subset using k-means clustering cohort selection based on the matching proximity is presented in this paper. SIFT and SURF are used as facial features to represent each face image and to calculate the similarity score between two face images. The clusters having highest and lowest centroid value are fused using union rule to form the target, user dependent cohort subset. The query-claimed matching scores are normalized with the help of T-norm cohort normalization technique. The scores after normalization are used in recognition separately for SIFT as well as SURF. Finally, the responses from the classifier for these two different features are fused at decision level to cover up the shortcomings of the cohort selection method if any. The experimental execution is done on FEI face database. This integrated face biometric system gains a significant hike in performance that evidences its effectiveness over baseline.