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 ℓ 1-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 w.r.t. 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.

Abstract — When using appearance-based recognition for self-localization of mobile robots, the images obtained during the exploration of the environment need to be efficiently stored in the memory. PCA offers means for representing the images in a low-dimensional subspace, which allows for efficient matching and recognition. For active exploration it is necessary to use an incremental method for the computation of the subspace. While such methods have been considered before, only the on-line construction of eigenvectors has been addressed. Representations of the images in the subspace were computed only after the final subspace had been built, requiring that all the images were kept in the memory. In this paper we propose to use an incremental PCA algorithm with the updating of partial image representations in a way that allows the robot to discard the acquired images immediately after the update. Such a model is open-ended, meaning that we can easily update it with new images. We show that the performance of the proposed method is comparable to the performance of the batch method in terms of compression, computational cost and the precision of localization. We also show that by applying the repetitive learning, the subspace converges to that constructed with the batch method. Keywords—Robot localization, on-line visual learning, PCA updating, view-based robot localization, repetitive learning. I.

Abstract—Linear subspace methods that provide sufficient reconstruction of the data, such as PCA, offer an efficient way of dealing with missing pixels, outliers, and occlusions that often appear in the visual data. Discriminative methods, such as LDA, which, on the other hand, are better suited for classification tasks, are highly sensitive to corrupted data. We present a theoretical framework for achieving the best of both types of methods: An approach that combines the discrimination power of discriminative methods with the reconstruction property of reconstructive methods which enables one to work on subsets of pixels in images to efficiently detect and reject the outliers. The proposed approach is therefore capable of robust classification with a high-breakdown point. We also show that subspace methods, such as CCA, which are used for solving regression tasks, can be treated in a similar manner. The theoretical results are demonstrated on several computer vision tasks showing that the proposed approach significantly outperforms the standard discriminative methods in the case of missing pixels and images containing occlusions and outliers. Index Terms—Subspace methods, reconstructive methods, discriminative methods, robust classification, robust regression,

{ matjaz.jogan,alesl} @ frimi-lj.si The results of recent studies on the possibility of spa-tial localization from panoramic images have shown good prospects for view-based methods. The major advantages of these methods are a wide field-of-view, capability of mod-elling cluttered environments, and flexibility in the learning phase. The redundant information captured in similar views is efficiently handled by the eigenspace approach. However, the standard approaches are sensitive to noise and occlu-sion. In this paper, we present a method of view-based lo-calization in a robust framework that solves these problems to a large degree. Experimental results on a large set of real panoramic images demonstrate the effectiveness of the approach and the level of achieved robustness. 1. Introduction and

Abstract. Appearance-based modeling of objects and scenes using PCA has been successfully applied in many recognition tasks. Robust methods which have made the recognition stage less susceptible to outliers, occlusions, and varying illumination have further enlarged the domain of applicability. However, much less research has been done in achieving robustness in the learning stage. In this paper, we propose a novel robust PCA method for obtaining a consistent subspace representation in the presence of outlying pixels in the training images. The method is based on the EM algorithm for estimation of principal subspaces in the presence of missing data. By treating the outlying points as missing pixels, we arrive at a robust PCA representation. We demonstrate experimentally that the proposed method is efficient. In addition, we apply the method to a set of panoramic images to build a representation that enables surveillance and view-based mobile robot localization. 1

While recognition of most facial variations, such as identity, expression and gender, has been exten-sively studied, automatic age estimation has rarely been explored. In contrast to other facial variations, aging variation presents several unique characteristics which make age estimation a challenging task. This paper proposes an automatic age estimation method named AGES (AGing pattErn Subspace). The basic idea is to model the aging pattern, which is defined as the sequence of a particular individual’s face images sorted in time order, by constructing a representative subspace. The proper aging pattern for a previously unseen face image is determined by the projection in the subspace that can reconstruct the face image with minimum reconstruction error, while the position of the face image in that aging pattern will then indicate its age. In the experiments, AGES and its variants are compared with the limited existing age estimation methods (WAS and AAS) and some well-established classification methods (kNN, BP, C4.5, and SVM). Moreover, a comparison with human perception ability on age is conducted. It is interesting to note that the performance of AGES is not only significantly better than that of all the other algorithms, but also comparable to that of the human observers.

We present a novel on-line conservative learning framework for an object detection system. All algorithms operate in an on-line mode, in particular we also present a novel on-line AdaBoost method. The basic idea is to start with a very simple object detection system and to exploit a huge amount of unlabeled video data by being very conservative in selecting training examples. The key idea is to use reconstructive and discriminative classifiers in an iterative co-training fashion to arrive at increasingly better object detectors. We demonstrate the framework on a surveillance task where we learn person detectors that are tested on two surveillance video sequences. We start with a simple moving object classifier and proceed with incremental PCA (on shape and appearance) as a reconstructive classifier which in turn generates a training set for a discriminative on-line AdaBoost classifier. 1.

An active object recognition system is built using three different uncertainty calculi: probability theory, possibility theory (or fuzzy logic) and Dempster-Shafer theory of evidence. The system is allowed to reposition the camera to capture additional views and, therefore, to resolve the classification result obtained from a single view. The approach uses an appearance based object representation, namely the parametric eigenspace, but the action planning steps are largely independent of any details of the specific object recognition environment. The active recognition problem can be tackled succesfully by all three approaches with sometimes only slight differences in performance. The results obtained in extensive test runs confirm that recognition rate can be improved considerably by performing active steps. Random selection of the next action is much less efficient than planning, both in recognition rate and in the average number of steps required for recognition. Remar...

Recently, we have proposed a new approach to estimation of the coefficients of eigenimages, which is robust against occlusion, varying background, and other types of non-Gaussian noise [4, 5]. In this paper we show that our method for estimating the coefficients can be applied to convolved and subsampled images yielding the same value of the coefficients. This enables an efficient multiresolution approach, where the values of the coefficients can directly be propagated through the scales. This property is used to extend our robust method to the problem of scaled images. We performed extensive experimental evaluations to confirm our theoretical results. 1 Introduction The appearance-based approaches to vision problems based on eigenspace analysis have shown the potential in many successful applications [10, 11, 15, 8, 14, 1]. The main advantage of these approaches is that the models encompass 2-D views which can easily be learned and enable to deal with combined effects of shape, refl...

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