Zihan Zhou

Toward a Practical Face Recognition System: Robust Alignment and Illumination by Sparse Representation

Many classic and contemporary face recognition algorithms work well on public data sets, but degrade sharply when they are used in a real recognition system. This is mostly due to the difficulty of simultaneously handling variations in illumination, image misalignment, and occlusion in the test image. We consider a scenario where the training images are well controlled and test images are only loosely controlled. We propose a conceptually simple face recognition system that achieves a high degree of robustness and stability to illumination variation, image misalignment, and partial occlusion. The system uses tools from sparse representation to align a test face image to a set of frontal training images. The region of attraction of our alignment algorithm is computed empirically for public face data sets such as Multi-PIE. We demonstrate how to capture a set of training images with enough illumination variation that they span test images taken under uncontrolled illumination. In order to evaluate how our algorithms work under practical testing conditions, we have implemented a complete face recognition system, including a projector-based training acquisition system. Our system can efficiently and effectively recognize faces under a variety of realistic conditions, using only frontal images under the proposed illuminations as training.

Stable Principal Component Pursuit

In this paper, we study the problem of recovering a low-rank matrix (the principal components) from a high-dimensional data matrix despite both small entry-wise noise and gross sparse errors. Recently, it has been shown that a convex program, named Principal Component Pursuit (PCP), can recover the low-rank matrix when the data matrix is corrupted by gross sparse errors. We further prove that the solution to a related convex program (a relaxed PCP) gives an estimate of the low-rank matrix that is simultaneously stable to small entry-wise noise and robust to gross sparse errors. More precisely, our result shows that the proposed convex program recovers the low-rank matrix even though a positive fraction of its entries are arbitrarily corrupted, with an error bound proportional to the noise level. We present simulation results to support our result and demonstrate that the new convex program accurately recovers the principal components (the low-rank matrix) under quite broad conditions. To our knowledge, this is the first result that shows the classical Principal Component Analysis (PCA), optimal for small i.i.d. noise, can be made robust to gross sparse errors; or the first that shows the newly proposed PCP can be made stable to small entry-wise perturbations.

Fast

l 1-minimization refers to finding the minimum l1-norm solution to an underdetermined linear system \mbib=A\mbix. Under certain conditions as described in compressive sensing theory, the minimum l1-norm solution is also the sparsest solution. In this paper, we study the speed and scalability of its algorithms. In particular, we focus on the numerical implementation of a sparsity-based classification framework in robust face recognition, where sparse representation is sought to recover human identities from high-dimensional facial images that may be corrupted by illumination, facial disguise, and pose variation. Although the underlying numerical problem is a linear program, traditional algorithms are known to suffer poor scalability for large-scale applications. We investigate a new solution based on a classical convex optimization framework, known as augmented Lagrangian methods. We conduct extensive experiments to validate and compare its performance against several popular l1-minimization solvers, including interior-point method, Homotopy, FISTA, SESOP-PCD, approximate message passing, and TFOCS. To aid peer evaluation, the code for all the algorithms has been made publicly available.

\ell_{1}

Most contemporary face recognition algorithms work well under laboratory conditions but degrade when tested in less-controlled environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, alignment, pose, and occlusion. In this paper, we propose a simple and practical face recognition system that achieves a high degree of robustness and stability to all these variations. We demonstrate how to use tools from sparse representation to align a test face image with a set of frontal training images in the presence of significant registration error and occlusion. We thoroughly characterize the region of attraction for our alignment algorithm on public face datasets such as Multi-PIE. We further study how to obtain a sufficient set of training illuminations for linearly interpolating practical lighting conditions. We have implemented a complete face recognition system, including a projector-based training acquisition system, in order to evaluate how our algorithms work under practical testing conditions. We show that our system can efficiently and effectively recognize faces under a variety of realistic conditions, using only frontal images under the proposed illuminations as training.

-Minimization Algorithms for Robust Face Recognition

Partially occluded faces are common in many applications of face recognition. While algorithms based on sparse representation have demonstrated promising results, they achieve their best performance on occlusions that are not spatially correlated (i.e. random pixel corruption). We show that such sparsity-based algorithms can be significantly improved by harnessing prior knowledge about the pixel error distribution. We show how a Markov Random Field model for spatial continuity of the occlusion can be integrated into the computation of a sparse representation of the test image with respect to the training images. Our algorithm efficiently and reliably identifies the corrupted regions and excludes them from the sparse representation. Extensive experiments on both laboratory and real-world datasets show that our algorithm tolerates much larger fractions and varieties of occlusion than current state-of-the-art algorithms.

Towards a practical face recognition system: Robust registration and illumination by sparse representation

This work builds on the method of to create a prototype access control system, capable of handling variations in illumination and expression, as well as significant occlusion or disguise. Our demonstration will allow participants to interact with the algorithm, gaining a better understanding strengths and limitations of sparse representation as a tool for robust recognition.

Face recognition with contiguous occlusion using markov random fields

Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel face recognition algorithm to address this problem based on a sparse representation based classification (SRC) framework. The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required training images to one sample per class. To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced. The SIT algorithms seek additional illumination examples of face images from one or more additional subject classes, and form an illumination dictionary. By enforcing a sparse representation of the query image, the method can recover and transfer the pose and illumination information from the alignment stage to the recognition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the existing algorithms in the single-sample regime and with less restrictions. In particular, the face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple training images, and the face recognition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization.

Demo: Robust face recognition via sparse representation

Recently, a new image deformation technique called content-preserving warping (CPW) has been successfully employed to produce the state-of-the-art video stabilization results in many challenging cases. The key insight of CPW is that the true image deformation due to viewpoint change can be well approximated by a carefully constructed warp using a set of sparsely constructed 3D points only. However, since CPW solely relies on the tracked feature points to guide the warping, it works poorly in large texture less regions, such as ground and building interiors. To overcome this limitation, in this paper we present a hybrid approach for novel view synthesis, observing that the texture less regions often correspond to large planar surfaces in the scene. Particularly, given a jittery video, we first segment each frame into piecewise planar regions as well as regions labeled as non-planar using Markov random fields. Then, a new warp is computed by estimating a single homography for regions belong to the same plane, while inheriting results from CPW in the non-planar regions. We demonstrate how the segmentation information can be efficiently obtained and seamlessly integrated into the stabilization framework. Experimental results on a variety of real video sequences verify the effectiveness of our method.

Single-Sample Face Recognition with Image Corruption and Misalignment via Sparse Illumination Transfer

We introduce a new approach to reconstructing accurate camera geometry and 3D models for urban structures in a holistic fashion, i.e., without relying on extraction or matching of traditional local features such as points and edges. Instead, we use semi-global or global features based on transform invariant low-rank textures, which are ubiquitous in urban scenes. Modern high-dimensional optimization techniques enable us to accurately and robustly recover precise and consistent camera calibration and scene geometry from single or multiple images of the scene. We demonstrate how to construct 3D models of large-scale buildings from sequences of multiple large-baseline uncalibrated images that conventional SFM systems do not apply.

Plane-Based Content Preserving Warps for Video Stabilization

In this paper, we show how two classical sparse recovery algorithms, Orthogonal Matching Pursuit and Basis Pursuit, can be naturally extended to recover block-sparse solutions for subspace-sparse signals. A subspace-sparse signal is sparse with respect to a set of subspaces, instead of atoms. By generalizing the notion of mutual incoherence to the set of subspaces, we show that all classical sufficient conditions remain exactly the same for these algorithms to work for subspace-sparse signals, in both noiseless and noisy cases. The sufficient conditions provided are easy to verify for large systems. We conduct simulations to compare the performance of the proposed algorithms.