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This paper proposes the use of the combination of digital curvelet transform and local binary patterns for recognizing facial expressions from still images. The curvelet transform is applied to the image of a face at a specific scale and orientation. Local binary patterns are extracted from the selected curvelet sub-bands to form the descriptive feature set of the expressions. The average of the features of a particular class of expression is considered as the representative feature set of that class. The expression recognition is performed using a nearest neighbor classifier with Chi-square as the dissimilarity metric. Experiments show that our method yields recognition rates of 93% and 90% in JAFFE and Cohn-Kanade databases respectively.