Acquiring height data from a single image of a face using local shape indicators

Castelán, Mario; Hancock, Edwin R.

doi:10.1016/j.cviu.2006.03.001

Cited by 18 publications

(9 citation statements)

References 26 publications

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“…Convexity-concavity ambiguities are unavoidable in single image shape-from-shading and are responsible for a number of illusions, including Gregory's famous inverted mask (Gregory 1997). Their effect typically manifests itself in shape-from-shading results by imploding the nose and exaggerating the cheeks (Castelán and Hancock 2006).…”

Section: Facial Shape-from-shadingmentioning

confidence: 98%

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

Smith

Hancock

2007

Int J Comput Vis

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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.

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Section: Facial Shape-from-shadingmentioning

confidence: 98%

“…For instance, both and Castelán and Hancock (2006) use the location of singular points to enforce convexity on the recovered surface. Zhao and Chellappa (2001), on the other hand, exploit the approximate bilateral symmetry of faces.…”

Section: Facial Shape-from-shadingmentioning

confidence: 99%

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

Smith

Hancock

2007

Int J Comput Vis

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“…However, it is extremely hard to find two perfect orthogonal images in the real life, so researchers have proposed new 3D modeling methods based on many multi-view images [18,19], which reduce the required condition but increase the difficult for registration. Secondly, the unsatisfactory modeling accuracy is usually achieved by the 3D face modeling methods based on single image such as SFS [5] technique and the methods based on statistic model [20]. Despite the fact that these methods can reconstruct 3D face using only one image, the modeling accuracy is low.…”

Section: Introductionmentioning

confidence: 98%

“…One popular method is shape from shading (SFS) [5,6]. It deals with the recovery of 3D surface from a single monocular image under the assumption of Lambertian model.…”

Section: Introductionmentioning

confidence: 99%

Rapid 3D face reconstruction by fusion of SFS and Local Morphable Model

Liao

Chen

Zhou

et al. 2012

Journal of Visual Communication and Image Representation

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“…Directly applying Shape-from-Shading (SFS) [17] to a single 2D image for face analysis is found to be very difficult, since the concave-convex ambiguity may lead to inversion of facial components [9]. To address this problem, one common approach is to impose domain specific constraints or use a face-specific shape model.…”

Section: Introductionmentioning

confidence: 99%

3D face texture modeling from uncalibrated frontal and profile images

Han

Jain

2012

2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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Abstract3D face modeling from 2D face images is of significant importance for face analysis, animation and recognition. Previous research on this topic mainly focused on 3D face modeling from a single 2D face image; however, a single face image can only provide a limited description of a 3D face. In many applications, for example, law enforcement, multi-view face images are usually captured for a subject during enrollment, which makes it desirable to build a 3D face texture model, given a pair of frontal and profile face images. We first determine the correspondence between uncalibrated frontal and profile face images through facial landmark alignment. An initial 3D face shape is then reconstructed from the frontal face image, followed by shape refinement utilizing the depth information provided by the profile image. Finally, face texture is extracted by mapping the frontal face image on the recovered 3D face shape. The proposed method is utilized for 2D face recognition in two scenarios: (i) normalization of probe image, and (ii) enhancing the representation capability of gallery set. Experimental results comparing the proposed method with a state-of-the-art commercial face matcher and densely sampled LBP on a subset of the FERET database show the effectiveness of the proposed 3D face texture model.

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Acquiring height data from a single image of a face using local shape indicators

Cited by 18 publications

References 26 publications

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

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

Rapid 3D face reconstruction by fusion of SFS and Local Morphable Model

3D face texture modeling from uncalibrated frontal and profile images

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