Elliptical higher-order-spectra periocular code

The periocular region has recently emerged as a standalone biometric trait, promising attractive trade-off between the iris alone and the entire face, especially for cases where neither the iris nor a full facial image can be acquired. This advantage provides another dimension for implementing a robust biometric system, performed in non-ideal conditions. Global features (LBP, HOG) and local features (SIFT) have been introduced; however, the performance of these features can deteriorate for images captured in unconstrained and less-cooperative conditions. A particular set of Higher Order Spectral (HOS) features have been proved to be invariant to translation, scale, rotation, brightness level shift and contrast change. These properties are desirable in the periocular recognition problem to deal with the non-ideal imaging conditions. This paper investigates the HOS features in different configurations for the periocular recognition problem under non-ideal conditions. Especially, we introduce a new sampling approach for the periocular region based on an elliptical coordinate. This non-linear sampling approach is then combined with the robustness of the HOS features for encoding the periocular region. In addition, we also propose a new technique for combining left and right periocular. The proposed feature-level fusion approach bases on state-of-the-art bilinear pooling technique to allow efficient interaction between the features of both perioculars. We show the validity of the proposed approach in encoding discriminant features, outperforming or comparing favorably with the state-of-the-art features on the two popular datasets: FRGC and JAFFE

Advanced Multilinear Data Analysis and Sparse Representation Approaches and Their Applications

Multifactor analysis plays an important role in data analysis since most real-world datasets usually exist with a combination of numerous factors. These factors are usually not independent but interdependent together. Thus, it is a mistake if a method only considers one aspect of the input data while ignoring the others. Although widely used, Multilinear PCA (MPCA), one of the leading multilinear analysis methods, still suffers from three major drawbacks. Firstly, it is very sensitive to outliers and noise and unable to cope with missing values. Secondly, since MPCA deals with huge multidimensional datasets, it is usually computationally expensive. Finally, it loses original local geometry structures due to the averaging process. This thesis sheds new light on the tensor decomposition problem via the ideas of fast low-rank approximation in random projection and tensor completion in compressed sensing. We propose a novel approach called Compressed Submanifold Multifactor Analysis (CSMA) to solve the three problems mentioned above. Our approach is able to deal with the problem of missing values and outliers via our proposed novel sparse Higher-order Singular Value Decomposition approach, named HOSVD-L1 decomposition. The Random Projection method is used to obtain the fast low-rank approximation of a given multifactor dataset. In addition, our method can preserve geometry of the original data. In the second part of this thesis, we present a novel pattern classification approach named Sparse Class-dependent Feature Analysis (SCFA), to connect the advantages of sparse representation in an overcomplete dictionary, with a powerful nonlinear classifier. The classifier is based on the estimation of class-specific optimal filters, by solving an L1-norm optimization problem using the Alternating Direction Method of Multipliers. Our method as well as its Reproducing Kernel Hilbert Space (RKHS) version is tolerant to the presence of noise and other variations in an image. Our proposed methods achieve very high classification accuracies in face recognition on two challenging face databases, i.e. the CMU Pose, Illumination and Expression (PIE) database and the Extended YALE-B that exhibit pose and illumination variations; and the AR database that has occluded images. In addition, they also exhibit robustness on other evaluation modalities, such as object classification on the Caltech101 database. Our method outperforms state-of-the-art methods on all these databases and hence they show their applicability to general computer vision and pattern recognition problems