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Face detection using local successive mean quantization transform (SMQT) features and the sparse network of winnows (SNoW) classifier has received interest in the computer vision community due to its success under varying illumination conditions. Recent work has also demonstrated the effectiveness of this classification technique for character recognition tasks. However, heavy storage requirements of the SNoW classifier necessitate the development of efficient techniques to reduce storage and computational requirements. This study shows that the SNoW classifier built with only a limited number of distinguishing SMQT features provides comparable performance to the original dense snow classifier. Initial results using the well-known CMU-MIT facial image database and a private character database are used to demonstrate the effectiveness of the proposed method.