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Face colour synthesis using partial least squares and the luminance-α-β colour transform

Face colour synthesis using partial least squares and the luminance-α-β colour transform

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For many tasks, it is necessary to synthesise realistic colour in faces from greyscale values. This is the problem the authors address in this study. Rather than propagating colour information in some regions of the image or transferring colour from an image source to a greyscale using some corresponding criterion, as many colouring systems attempt to do, they seek to synthesise facial colour information using a database of examples. This methodology is divided into two main stages. In the first stage the facial skin tone is predicted through the multiple linear regression method known as partial least squares. This regression allows to define a linear transformation between facial greyscale and colour subspaces. The second stage involves the luminance-α-β (Lαβ) colour transform which is responsible for the recovery of the fine facial detail. The core of the proposed methodology is the combination of statistical subspace analysis with the appropriate colour transform so as to produce realistic facial colourisation results in a direct manner.

Inspec keywords: regression analysis; image colour analysis; face recognition; transforms; least squares approximations

Other keywords: face colour synthesis; statistical subspace analysis; linear transformation; luminance-α-β colour transform; partial least squares; facial colour information; greyscale values; image source; colouring systems; multiple linear regression method

Subjects: Other topics in statistics; Other topics in statistics; Integral transforms; Interpolation and function approximation (numerical analysis); Integral transforms; Image recognition; Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques

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