Facial Shape-from-shading and Recognition Using Principal Geodesic Analysis and Robust Statistics

• Published in: International journal of computer vision Vol. 76; no. 1; pp. 71 - 91
• Main Authors: Smith, William A. P., Hancock, Edwin R.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.01.2008; Springer; Springer Nature B.V
• Subjects: Applied sciences; Artificial Intelligence; Computer Imaging; Computer Science; Computer science; control theory; systems; Exact sciences and technology; Image Processing and Computer Vision; Pattern Recognition; Pattern Recognition and Graphics; Pattern recognition. Digital image processing. Computational geometry; Statistics; Vision; Face recognition; Shape-from-shading; Principal geodesic analysis; Image recognition; Statistical analysis; Probabilistic approach; Similarity; Image processing; Gaussian distribution; Geodesic structure; Pattern recognition; Parameterization; Modeling; Surface normal; Luminance; Albedo; Facies; Geodesic; Illumination; Pattern recovery; Shadowing
• ISSN: 0920-5691; 1573-1405
• DOI: 10.1007/s11263-007-0074-8

The aim in this paper is to use principal geodesic analysis to model the statistical variations for sets of facial needle maps. We commence by showing how to represent the distribution of surface normals using the exponential map. Shape deformations are described using principal geodesic analysis on the exponential map. Using ideas from robust statistics we show how this deformable model may be fitted to facial images in which there is significant self-shadowing. Moreover, we demonstrate that the resulting shape-from-shading algorithm can be used to recover accurate facial shape and albedo from real world images. In particular, the algorithm can effectively fill-in the facial surface when more than 30% of its area is subject to self-shadowing. To investigate the utility of the shape parameters delivered by the method, we conduct experiments with illumination insensitive face recognition. We present a novel recognition strategy in which similarity is measured in the space of the principal geodesic parameters. We also use the recovered shape information to generate illumination normalized prototype images on which recognition can be performed. Finally we show that, from a single input image, we are able to generate the basis images employed by a number of well known illumination-insensitive recognition algorithms. We also demonstrate that the principal geodesics provide an efficient parameterization of the space of harmonic basis images.