@conference {12490,
	title = {The role of geometry in age estimation},
	booktitle = {2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)},
	year = {2010},
	month = {2010/03/14/19},
	pages = {946 - 949},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Understanding and modeling of aging in human faces is an important problem in many real-world applications such as biometrics, authentication, and synthesis. In this paper, we consider the role of geometric attributes of faces, as described by a set of landmark points on the face, in age perception. Towards this end, we show that the space of landmarks can be interpreted as a Grassmann manifold. Then the problem of age estimation is posed as a problem of function estimation on the manifold. The warping of an average face to a given face is quantified as a velocity vector that transforms the average to a given face along a smooth geodesic in unit-time. This deformation is then shown to contain important information about the age of the face. We show in experiments that exploiting geometric cues in a principled manner provides comparable performance to several systems that utilize both geometric and textural cues. We show results on age estimation using the standard FG-Net dataset and a passport dataset which illustrate the effectiveness of the approach.},
	keywords = {age estimation, Aging, Biometrics, computational geometry, Face, Face Geometry, Facial animation, Feature extraction, function estimation problem, geometric face attributes, Geometry, Grassmann manifold, human face modeling, human face understanding, HUMANS, Mouth, regression, Regression analysis, SHAPE, Solid modeling, solid modelling, velocity vector},
	isbn = {978-1-4244-4295-9},
	doi = {10.1109/ICASSP.2010.5495292},
	author = {Turaga,P. and Biswas,S. and Chellapa, Rama}
}
@article {12544,
	title = {Model Driven Segmentation of Articulating Humans in Laplacian Eigenspace},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {30},
	year = {2008},
	month = {2008/10//},
	pages = {1771 - 1785},
	abstract = {We propose a general approach using Laplacian Eigenmaps and a graphical model of the human body to segment 3D voxel data of humans into different articulated chains. In the bottom-up stage, the voxels are transformed into a high-dimensional (6D or less) Laplacian Eigenspace (LE) of the voxel neighborhood graph. We show that LE is effective at mapping voxels on long articulated chains to nodes on smooth 1D curves that can be easily discriminated, and prove these properties using representative graphs. We fit 1D splines to voxels belonging to different articulated chains such as the limbs, head and trunk, and determine the boundary between splines using the spline fitting error. A top-down probabilistic approach is then used to register the segmented chains, utilizing their mutual connectivity and individual properties. Our approach enables us to deal with complex poses such as those where the limbs form loops. We use the segmentation results to automatically estimate the human body models. While we use human subjects in our experiments, the method is fairly general and can be applied to voxel-based segmentation of any articulated object composed of long chains. We present results on real and synthetic data that illustrate the usefulness of this approach.},
	keywords = {3D voxel data segmentation, algorithms, Anatomic;Pattern Recognition, Artificial intelligence, Automated;Reproducibility of Results;Sensitivity and Specificity;Whole Body Imaging;, Computer simulation, Computer-Assisted;Imaging, curve fitting, Eigenvalues and eigenfunctions, graph theory, human articulation, human body graphical model, human motion analysis, HUMANS, Image Enhancement, Image Interpretation, Image motion analysis, image registration, Image segmentation, Laplace transforms, Laplacian eigenspace transform, model driven segmentation, probability, representative graph, skeleton registration, Smoothing methods, solid modelling, spline fitting error, splines (mathematics), Three-Dimensional;Joints;Models, top-down probabilistic approach, voxel neighborhood graph},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2007.70823},
	author = {Sundaresan, A. and Chellapa, Rama}
}
