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Face recognition is a challenging problem, especially when low resolution images or image sequences are used for the task. Many methods have been proposed that can combine multiple low resolution images to realize a higher resolution or super-resolved image. Nonetheless, their utility and limitations for use in face recognition are not well understood. In this paper, we present a quantitative and comparative evaluation of wavelet transform based methods for image super-resolution. We evaluate different basis functions, varying levels of decomposition, and multiple methods for coefficient fusion to maximize the benefit of the super-resolved image for the task of face recognition. We have used a Discrete Wavelet Transform and the shift-invariant Dual-Tree Complex Wavelet Transform. Results are reported across both manually generated datasets and data from a surveillance system.