23,568 research outputs found
Pose-Invariant 3D Face Alignment
Face alignment aims to estimate the locations of a set of landmarks for a
given image. This problem has received much attention as evidenced by the
recent advancement in both the methodology and performance. However, most of
the existing works neither explicitly handle face images with arbitrary poses,
nor perform large-scale experiments on non-frontal and profile face images. In
order to address these limitations, this paper proposes a novel face alignment
algorithm that estimates both 2D and 3D landmarks and their 2D visibilities for
a face image with an arbitrary pose. By integrating a 3D deformable model, a
cascaded coupled-regressor approach is designed to estimate both the camera
projection matrix and the 3D landmarks. Furthermore, the 3D model also allows
us to automatically estimate the 2D landmark visibilities via surface normals.
We gather a substantially larger collection of all-pose face images to evaluate
our algorithm and demonstrate superior performances than the state-of-the-art
methods
Extension of Sparse Randomized Kaczmarz Algorithm for Multiple Measurement Vectors
The Kaczmarz algorithm is popular for iteratively solving an overdetermined
system of linear equations. The traditional Kaczmarz algorithm can approximate
the solution in few sweeps through the equations but a randomized version of
the Kaczmarz algorithm was shown to converge exponentially and independent of
number of equations. Recently an algorithm for finding sparse solution to a
linear system of equations has been proposed based on weighted randomized
Kaczmarz algorithm. These algorithms solves single measurement vector problem;
however there are applications were multiple-measurements are available. In
this work, the objective is to solve a multiple measurement vector problem with
common sparse support by modifying the randomized Kaczmarz algorithm. We have
also modeled the problem of face recognition from video as the multiple
measurement vector problem and solved using our proposed technique. We have
compared the proposed algorithm with state-of-art spectral projected gradient
algorithm for multiple measurement vectors on both real and synthetic datasets.
The Monte Carlo simulations confirms that our proposed algorithm have better
recovery and convergence rate than the MMV version of spectral projected
gradient algorithm under fairness constraints
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