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Abstract This paper presents an efficient facial expression recognition method by measuring the histogram distance based on preprocessing. The preprocessing that uses both centroid shift and histogram equalization is applied to improve the recognition performance, The distance measurement is also applied to estimate the similarity between the facial expressions. The centroid shift based on the first moment balan ce technique is applied not only to obtain the robust recognition with respect to position or size variations but al so to reduce the distance measurement load by excluding the background in the recognition. Histogram equalization is used f or robustly recognizing the poor contrast of the images due to light intensity. The prop osed method has been applied for recognizing 72 facial expression images(4 persons*18 scenes) of 320*243 pixels. Three distances such as city-block, Euclidean, and ordinal are used as a similarity measure between histograms. The experimental results show that the prop osed method has superior recognition performances compared with the method without preprocessing. The ordinal dis tance shows superior recognition performances over city-block and Euclidean distances, respectively.Key Words : Histogram Distance Measurement, Centroid Shift, Histogram Eq ualization, Facial Expression Recognition

An Efficient Facial Expression Recognition by Measuring Histogram Distance Based on Preprocessing