Three Dimensional Face Recognition Using Iso-Geodesic and Iso-Depth Curves

Jahanbin, Sina; Choi, Hyun-Jun; Liu, Yang; Bovik, Alan C.

doi:10.1109/btas.2008.4699378

Cited by 25 publications

(18 citation statements)

References 9 publications

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“…The final step is to calculate a dissimilarity between the invariant representations in order to be able to compare faces, using a dissimilarity measure on features of the representation [60], [102], [104]- [108], [110], [111], [114], a dissimilarity measure on statistical model coefficients [57], [103], [109], [112], [116], or a dissimilarity measure on the matching process of the invariant representations [113], [117], [118].…”

Section: Isometric Deformation Modelingmentioning

confidence: 99%

“…The mostly used isometric deformation-invariant representations are isogeodesics, curves containing points on an equal geodesic distance to a reference point (nose tip), as in [60], [102], [104], [105], [107], [108], and [115]. Vectors with geodesic distances from points in a well-defined order are related to the nose tip [111].…”

Section: Isometric Deformation Modelingmentioning

confidence: 99%

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A Comparative Study of 3-D Face Recognition Under Expression Variations

Smeets

Claes

Hermans

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

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Research in face recognition has continuously been challenged by extrinsic (head pose, lighting conditions) and intrinsic (facial expression, aging) sources of variability. While many survey papers on face recognition exist, in this paper, we focus on a comparative study of 3-D face recognition under expression variations. As a first contribution, 3-D face databases with expressions are listed, and the most important ones are briefly presented and their complexity is quantified using the iterative closest point (ICP) baseline recognition algorithm. This allows to rank the databases according to their inherent difficulty for face-recognition tasks. This analysis reveals that the FRGC v2 database can be considered as the most challenging because of its size, the presence of expressions and outliers, and the time lapse between the recordings. Therefore, we recommend to use this database as a reference database to evaluate (expression-invariant) 3-D face-recognition algorithms. We also determine and quantify the most important factors that influence the performance. It appears that performance decreases 1) with the degree of nonfrontal pose, 2) for certain expression types, 3) with the magnitude of the expressions, 4) with an increasing number of expressions, and 5) for a higher number of gallery subjects. Future 3-D face-recognition algorithms should be evaluated on the basis of all these factors. As the second contribution, a survey of published 3-D face-recognition methods that deal with expression variations is given. These methods are subdivided into three classes depending on the way the expressions are handled. Region-based methods use expression-stable regions only, while other methods model the expressions either using an isometric or a statistical model. Isometric models assume the deformation because of expression variation to be (locally) isometric, meaning that the deformation preserves lengths along the surface. Statistical models learn how the facial soft tissue deforms during expressions based on a training database with expression labels. Algorithmic performances are evaluated by the comparison of recognition rates for identification and verification. No statistical significant differences in class performance are found between any pair of classes.

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Section: Isometric Deformation Modelingmentioning

confidence: 99%

Section: Isometric Deformation Modelingmentioning

confidence: 99%

A Comparative Study of 3-D Face Recognition Under Expression Variations

Smeets

Claes

Hermans

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

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show abstract

“…Mpiperis et al (2007) map the iso-geodesic curves to concentric circles on a plane using a piecewise linear warping transformation. Jahanbin et al (2008) extract five shape descriptors from each iso-geodesic: convexity, ratio of principal axes, compactness, circular and elliptic variance. These features are trained with Linear Discriminant Analysis (LDA).…”

Section: Contour and Profile Curvesmentioning

confidence: 99%

Surface representations for 3D face recognition

Fabry¹,

Smeets²,

Vandermeulen³

2010

Face Recognition

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“…Deformed circular patterns represent 3D geometric information of an object surface. Since facial curves represent geometric information of an object, these have been used for classification for a recognition as well, for instance, geodesic curves (Feng et al 2007;Miao and Krim 2010;Jahanbin et al 2008 andBerretti et al 2010). For example, previous algorithms used iso-geodesic curves which represent 3D geometric information of objects, and investigated integral invariant signatures (Feng et al 2007), the evolution of geodesic curves (Miao and Krim 2010).…”

Section: Introductionmentioning

confidence: 99%

3D face recognition in the Fourier domain using deformed circular curves

Lee

Krim

2015

Multidim Syst Sign Process

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One of the most significant problems in image and vision applications is the efficient representation of a target image containing a large amount of data with high complexity. The ability to analyze high dimensional signals in a lower dimension without losing their information, has been crucial in the field of image processing. This paper proposes an approach to 3D face recognition using dimensionality reduction based on deformed circular curves, on the shortest geodesic distances, and on the properties of the Fourier Transform. Measured geodesic distances information generates a matrix whose entities are geodesic distances between the reference point and an arbitrary point on a 3D object, and an onedimensional vector is generated by reshaping the matrix without losing the original properties of the target object. Following the property of the Fourier Transform, symmetry of the magnitude response, the original signal can be analyzed in the lower dimensional space without loss of inherent characteristics. This paper mainly deal with the efficient representation and recognition algorithm using deformed circular curves and the simulation shows promising result for recognition of geometric face information.

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Three Dimensional Face Recognition Using Iso-Geodesic and Iso-Depth Curves

Cited by 25 publications

References 9 publications

A Comparative Study of 3-D Face Recognition Under Expression Variations

A Comparative Study of 3-D Face Recognition Under Expression Variations

Surface representations for 3D face recognition

3D face recognition in the Fourier domain using deformed circular curves

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