Raw image data offer rich source of information for matching and classification. For simplicity of pattern recognition system design, a sequential approach consisting of sensing, feature extraction and matching is conventionally adopted where each stage transforms a particular component of information relatively independently. The interaction between these modules is limited. Some of the errors in the end-to-end sequential processing can be easily eliminated especially for the feature extraction stage by revisiting the original image data. We propose a feedback path for the feature extraction stage, followed by a feature refinement stage for improving the matching performance. This performance improvement is illustrated in the context of a minutiae-based fingerprint verification system. We show that a minutia verification stage based on reexamining the gray-scale profile in a detected minutia's spatial neighborhood in the sensed image can improve the matching performance by 4% on our database. Further, we show that a feature refinement stage, which assigns a class label to each detected minutia (ridge ending and ridge bifurcation) before matching can also improve the matching performance by 3%. A combination of feedback (minutia verification) in the feature extraction phase and feature refinement (minutia classification) improves the overall performance of the fingerprint verification system by 8%.