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This paper investigates the performance of a Daubechies Wavelet family in recognizing facial expressions. A set of luminance stickers were fixed on subject's face and the subject is instructed to perform required facial expressions. At the same time, subject's expressions are recorded in video. A set of 2D coordinate values are obtained by tracking the movements of the stickers in video using tracking software. Daubechies wavelet transform with different orders (db1 to db20) performed on obtained data. Standard deviation is derived from wavelet approximation coefficients for each daubechies wavelet orders. This standard deviation is used as an input to the neural network for classifying 8 facial expressions.