Robust Face Recognition via Sparse Representation
TLDR
This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.Abstract:
We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.read more
Citations
More filters
Journal ArticleDOI
Regularized Kernel Discriminant Analysis With a Robust Kernel for Face Recognition and Verification
TL;DR: This work shows, for the first time, how to perform the eigen analysis of the within-class scatter matrix directly in the feature space, and proposes a kernel discriminant analysis (KDA) which combines eigenspectrum regularization with a feature-level scheme (ER-KDA).
Journal ArticleDOI
Image Classification With Tailored Fine-Grained Dictionaries
TL;DR: A novel fine-grained dictionary learning method for image classification that fills the gap between the patterns in CSDs and UD, and identifies the most discriminative FSD for each class by minimizing the sparse reconstruction error.
Journal ArticleDOI
Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification
TL;DR: Experiments on four real HSI datasets demonstrate the qualitative and quantitative superiority of the proposed multiscale superpixel-based sparse representation algorithm over several well-known classifiers.
Proceedings ArticleDOI
Noise robust exemplar-based connected digit recognition
Jort F. Gemmeke,Tuomas Virtanen +1 more
TL;DR: A noise robust exemplar-based speech recognition system where noisy speech is modeled as a linear combination of a set of speech and noise exemplars, which proves to be promising, achieving up to 55.8% accuracy at signal-to-noise ratio −5 dB on the AURORA-2 connected digit recognition task.
Journal ArticleDOI
Collaborative Graph-Based Discriminant Analysis for Hyperspectral Imagery
TL;DR: Experimental results demonstrate that the proposed collaborative approach can yield even better classification performance than the previous state-of-the-art sparsity-based approach with much lower computational cost.
References
More filters
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
Convex Optimization
Stephen Boyd,Lieven Vandenberghe +1 more
TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.