1,830 research outputs found
Mixtures of Local Linear Subspaces for Face Recognition
Traditional subspace methods for face recognition compute a measure of similarity between images after projecting them onto a fixed linear subspace that is spanned by some principal component vectors (a.k.a. "eigenfaces") of a training set of images. By supposing a parametric Gaussian distribution over the subspace and a symmetric Gaussian noise model for the image given a point in the subspace, we can endow this framework with a probabilistic interpretation so that Bayes-optimal decisions can be made. However, we expect that different image clusters (corresponding, say, to different poses and expressions) will be best represented by different subspaces. In this paper, we study the recognition performance of a mixture of local linear subspaces model that can be fit to training data using the expectation maximization algorithm. The mixture model outperforms a nearest-neighbor classifier that operates in a PCA subspace. 1 Introduction Presented at Computer Vision and Pattern Recognitio..
Median K-flats for hybrid linear modeling with many outliers
We describe the Median K-Flats (MKF) algorithm, a simple online method for
hybrid linear modeling, i.e., for approximating data by a mixture of flats.
This algorithm simultaneously partitions the data into clusters while finding
their corresponding best approximating l1 d-flats, so that the cumulative l1
error is minimized. The current implementation restricts d-flats to be
d-dimensional linear subspaces. It requires a negligible amount of storage, and
its complexity, when modeling data consisting of N points in D-dimensional
Euclidean space with K d-dimensional linear subspaces, is of order O(n K d D+n
d^2 D), where n is the number of iterations required for convergence
(empirically on the order of 10^4). Since it is an online algorithm, data can
be supplied to it incrementally and it can incrementally produce the
corresponding output. The performance of the algorithm is carefully evaluated
using synthetic and real data
Multi-View Face Recognition From Single RGBD Models of the Faces
This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks
Kernel Spectral Curvature Clustering (KSCC)
Multi-manifold modeling is increasingly used in segmentation and data
representation tasks in computer vision and related fields. While the general
problem, modeling data by mixtures of manifolds, is very challenging, several
approaches exist for modeling data by mixtures of affine subspaces (which is
often referred to as hybrid linear modeling). We translate some important
instances of multi-manifold modeling to hybrid linear modeling in embedded
spaces, without explicitly performing the embedding but applying the kernel
trick. The resulting algorithm, Kernel Spectral Curvature Clustering, uses
kernels at two levels - both as an implicit embedding method to linearize
nonflat manifolds and as a principled method to convert a multiway affinity
problem into a spectral clustering one. We demonstrate the effectiveness of the
method by comparing it with other state-of-the-art methods on both synthetic
data and a real-world problem of segmenting multiple motions from two
perspective camera views.Comment: accepted to 2009 ICCV Workshop on Dynamical Visio
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