1,801 research outputs found
Supervised Sparsity Preserving Projections for Face Recognition
Recently feature extraction methods have commonly been used as a principled approach to understand the intrinsic structure hidden in high-dimensional data. In this paper, a novel supervised learning method, called Supervised Sparsity Preserving Projections (SSPP), is proposed. SSPP attempts to preserve the sparse representation structure of the data when identifying an efficient discriminant subspace. First, SSPP creates a concatenated dictionary by class-wise PCA decompositions and learns the sparse representation structure of each sample under the constructed dictionary using the least squares method. Second, by maximizing the ratio of non-local scatter to local scatter, a Laplacian discriminant function is defined to characterize the separability of the samples in the different sub-manifolds. Then, to achieve improved recognition results, SSPP integrates the learned sparse representation structure as a regular term into the Laplacian discriminant function. Finally, the proposed method is converted into a generalized eigenvalue problem. The extensive and promising experimental results on several popular face databases validate the feasibility and effectiveness of the proposed approach
FACE RECOGNITION AND VERIFICATION IN UNCONSTRAINED ENVIRIONMENTS
Face recognition has been a long standing problem in computer vision. General
face recognition is challenging because of large appearance variability due to
factors including pose, ambient lighting, expression, size of the face, age, and distance
from the camera, etc. There are very accurate techniques to perform face
recognition in controlled environments, especially when large numbers of samples
are available for each face (individual). However, face identification under uncontrolled(
unconstrained) environments or with limited training data is still an unsolved
problem. There are two face recognition tasks: face identification (who is who in
a probe face set, given a gallery face set) and face verification (same or not, given
two faces). In this work, we study both face identification and verification in unconstrained
environments.
Firstly, we propose a face verification framework that combines Partial Least
Squares (PLS) and the One-Shot similarity model[1]. The idea is to describe a
face with a large feature set combining shape, texture and color information. PLS
regression is applied to perform multi-channel feature weighting on this large feature
set. Finally the PLS regression is used to compute the similarity score of an image
pair by One-Shot learning (using a fixed negative set).
Secondly, we study face identification with image sets, where the gallery and
probe are sets of face images of an individual. We model a face set by its covariance
matrix (COV) which is a natural 2nd-order statistic of a sample set.By exploring an
efficient metric for the SPD matrices, i.e., Log-Euclidean Distance (LED), we derive
a kernel function that explicitly maps the covariance matrix from the Riemannian
manifold to Euclidean space. Then, discriminative learning is performed on the
COV manifold: the learning aims to maximize the between-class COV distance and
minimize the within-class COV distance.
Sparse representation and dictionary learning have been widely used in face
recognition, especially when large numbers of samples are available for each face
(individual). Sparse coding is promising since it provides a more stable and discriminative
face representation. In the last part of our work, we explore sparse
coding and dictionary learning for face verification application. More specifically,
in one approach, we apply sparse representations to face verification in two ways
via a fix reference set as dictionary. In the other approach, we propose a dictionary
learning framework with explicit pairwise constraints, which unifies the discriminative
dictionary learning for pair matching (face verification) and classification (face
recognition) problems
- …