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This paper describes a multiresolution image-matching strategy, based on the Complex Discrete Wavelet Transform (CDWT), to derive a dense disparity field with hierarchical (coarse-to-fine) refinement. The CDWT feature space efficiently provides fractionally accurate matching results which are robust to typical image formation perturbations such as offsets, global scaling, and additive noise. At each level of the hierarchy, the disparity field is regularised to provide a global compromise between feature similarity and disparity field continuity, resulting in feature-sensitive smoothing. The algorithm is well suited to analysing facial images, for which we demonstrate striking reconstruction results.

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