Abstract—Human faces undergo considerable amounts of variations with aging. While face recognition systems have been proven to be sensitive to factors such as illumination and pose, their sensitivity to facial aging effects is yet to be studied. How does age progression affect the similarity between a pair of face images of an individual? What is the confidence associated with establishing the identity between a pair of age separated face images? In this paper, we develop a Bayesian age difference classifier that classifies face images of individuals based on age differences and performs face verification across age progression. Further, we study the similarity of faces across age progression. Since age separated face images invariably differ in illumination and pose, we propose preprocessing methods for minimizing such variations. Experimental results using a database comprising of pairs of face images that were retrieved from the passports of 465 individuals are presented. The verification system for faces separated by as many as nine years, attains an equal error rate of 8.5%. Index Terms—Age progression, face recognition, face verification, probabilistic eigenspaces, similarity measure. I.

One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples. Fewer samples per person mean less laborious effort for collecting them, lower costs for storing and processing them. Unfortunately, many reported face recognition techniques rely heavily on the size and representative of training set, and most of them will suffer serious performance drop or even fail to work if only one training sample per person is available to the systems. This situation is called “one sample per person ” problem: given a stored database of faces, the goal is to identify a person from the database later in time in any different and unpredictable poses, lighting, etc from just one image. Such a task is very challenging for most current algorithms due to the extremely limited representative of training sample. Numerous techniques have been developed to attack this problem, and the purpose of this paper is to categorize and evaluate these algorithms. The prominent algorithms are described and critically analyzed. Relevant issues such as data collection, the influence of the small sample size, and system evaluation are discussed, and several promising directions for future research are also proposed in this paper.

In this paper, we present a new method to change the illumination condition of a face image, with unknown face geometry and albedo information. This problem is particularly difficult when there is only one single image of the subject available and it was taken under a harsh lighting condition. Recent research demonstrates that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace using spherical harmonic representation. However, the approximation error can be large under harsh lighting conditions [2] thus making it difficult to recover albedo information. In order to address this problem, we propose a subregion based framework that uses a Markov Random Field to model the statistical distribution and spatial coherence of face texture, which makes our approach not only robust to harsh lighting conditions, but insensitive to partial occlusions as well. The performance of our framework is demonstrated through various experimental results, including the improvement to the face recognition rate under harsh lighting conditions. 1.

We analyze the usage of Speeded Up Robust Features (SURF) as local descriptors for face recognition. The effect of different feature extraction and viewpoint consistency constrained matching approaches are analyzed. Furthermore, a RANSAC based outlier removal for system combination is proposed. The proposed approach allows to match faces under partial occlusions, and even if they are not perfectly aligned or illuminated. Current approaches are sensitive to registration errors and usually rely on a very good initial alignment and illumination of the faces to be recognized. A grid-based and dense extraction of local features in combination with a block-based matching accounting for different viewpoint constraints is proposed, as interest-point based feature extraction approaches for face recognition often fail. The proposed SURF descriptors are compared to SIFT descriptors. Experimental results on the AR-Face and CMU-PIE database using manually aligned faces, unaligned faces, and partially occluded faces show that the proposed approach is robust and can outperform current generic approaches.

Abstract — The pose detection of three-dimensional (3-D) objects from two-dimensional (2-D) images is an important issue in computer vision and robotics applications. Specific examples include automated assembly, automated part inspection, robotic welding, and human robot interaction, as well as a host of others. Eigendecomposition is a common technique for dealing with this issue and has been applied to sets of correlated images for this purpose. Unfortunately, for the pose detection of 3-D objects, a very large number of correlated images must be captured from many different orientations. As a result, the eigendecomposition of this large set of images is very computationally expensive. In this work, we present a method for capturing images of objects from many locations by sampling S 2 appropriately. Using this spherical sampling pattern, the computational burden of computing the eigendecomposition can be reduced by using the Spherical Harmonic Transform to “condense” information due to the correlation in S 2. We propose a computationally efficient algorithm for approximating the eigendecomposition based on the spherical harmonic transform analysis. Experimental results are presented to compare and contrast the algorithm against the true eigendecomposition, as well as quantify the computational savings. I.

In this paper, we consider pose estimation of 3-D objects from an aerial perspective using eigendecomposition. We first outline a sampling method to acquire images of the object from an aerial view by sampling the upper unit hemisphere. Using this hemispherical sampling pattern, the computational burden of computing the eigendecomposition can be reduced by using the HemiSpherical Harmonic Transform (HSHT) to “condense ” information due to the hemispherical correlation. We then propose a computationally efficient algorithm for approximating the eigendecomposition based on the HSHT analysis. 1.

In this paper, we propose a non-stationary stochastic filtering framework for the task of albedo estimation from a single image. There are several approaches in literature for albedo estimation, but few include the errors in estimates of surface normals and light source directions to improve the albedo estimate. The proposed approach effectively utilizes the error statistics of surface normals and illumination direction for robust estimation of albedo. The albedo estimate obtained is further used to generate albedo-free normalized images for recovering the shape of an object. Illustrations and experiments are provided to show the efficacy of the approach and its application to illumination-invariant matching and shape recovery. 1.

Abstract—Determining the pose of a three-dimensional object under unknown lighting conditions is a challenging problem. Eigenspace methods represent one computationally efficient method for doing illumination invariant pose estimation, and have been applied in a variety of application domains. Unfortunately, determining the appropriate eigenspace dimension, as well as the eigenspace itself, is computationally prohibitive for real-world applications. This paper presents a method to reduce this expense by using results from spectral theory. In particular, this paper shows that a set of images of an object under a wide range of illumination conditions and a fixed pose can be significantly reduced by projecting this data on to a few low-frequency spherical harmonics, producing a set of “harmonic images”. It is then shown that the dimensionality of the set of harmonic images can be further reduced by utilizing the Fast Fourier Transform. An eigendecomposition is then applied in the spectral domain thus relieving the computational burden. Experimental results are presented to compare the proposed algorithm to the true eigendecomposition, as well as assess the computational savings. Index Terms—Eigenspace Decomposition, pose estimation, illumination variation, spherical harmonics, Fourier transforms. I.

Abstract—Existing learning-based face super-resolution (hallucination) techniques generate high-resolution images of a single facial modality (i.e., at a fixed expression, pose and illumination) given one or set of low-resolution face images as probe. Here, we present a generalized approach based on a hierarchical tensor (multilinear) space representation for hallucinating high-resolution face images across multiple modalities, achieving generalization to variations in expression and pose. In particular, we formulate a unified tensor which can be reduced to two parts: a global image-based tensor for modeling the mappings among different facial modalities, and a local patch-based multiresolution tensor for incorporating high-resolution image details. For realistic hallucination of unregistered low-resolution faces contained in raw images, we develop an automatic face alignment algorithm capable of pixel-wise alignment by iteratively warping the probing face to its projection in the space of training face images. Our experiments show not only performance superiority over existing benchmark face super-resolution techniques on single modal face hallucination, but also novelty of our approach in coping with multimodal hallucination and its robustness in automatic alignment under practical imaging conditions. Index Terms—Face hallucination, super-resolution, tensor. I. PROBLEM STATEMENT

A number of methods in tracking and recognition have successfully exploited lowdimensional representations of object appearance learned from a set of examples. In all these approaches, the construction of the underlying low-dimensional manifold relied upon obtaining different instances of the object’s appearance and then using statistical data analysis tools to approximate the appearance space. This requires collecting a very large number of examples and the accuracy of the method depends upon the examples that have been chosen. In this chapter, we show that it is possible to estimate low-dimensional manifolds that describe object appearance using a combination of analytically derived geometrical models and statistical data analysis. Specifically, we derive a quadrilinear space of object appearance that is able to represent the effects of illumination, motion, identity and shape. We then show how efficient tracking algorithms like inverse compositional estimation can be adapted to the geometry of this manifold. Our proposed method significantly reduces the amount of data that needs to be collected for learning the manifolds and makes the