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Abstract: In the last decade, many variants of classical linear discriminant analysis (LDA) have been developed to tackle the under-sampled problem in face recognition. However, choosing the variants is not easy since these methods involve eigenvalue decomposition that makes cross-validation computationally expensive. In this paper, we propose to solve this problem by unifying these LDA variants in one framework: principal component analysis (PCA) plus constrained ridge regression (CRR). In CRR, one selects the target (also called class indicator) for each class, and finds a projection to locate the class centers at their class targets and the transform minimizes the within-class distances with a penalty on the transform norm as in ridge regression. Under this framework, many existing LDA methods can be viewed as PCA+CRR with particular regularization numbers and class indicators and we propose to choose the best LDA method as choosing the best member from the CRR family. The latter can be done by comparing their leave-one-out (LOO) errors and we present an efficient algorithm, which requires similar computations to the training process of CRR, to evaluate the LOO errors. Experiments on Yale Face B, Extended Yale B and CMU-PIE databases are conducted to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we propose a new optimization framework that unites some of the existing tensor based methods for face recognition on a common mathematical basis. Tensor based approaches rely on the ability to decompose an image into its constituent factors (i.e. person, lighting, viewpoint, etc.) and then utilizing these factor spaces for recognition. We first develop a multilinear optimization problem relating an image to its constituent factors and then develop our framework by formulating a set of strategies that can be followed to solve this optimization problem. The novelty of our research is that the proposed framework offers an effective methodology for explicit non-empirical comparison of the different tensor methods as well as providing a way to determine the applicability of these methods in respect to different recognition scenarios. Importantly, the framework allows the comparative analysis on the basis of quality of solutions offered by these methods. Our theoretical contribution has been validated by extensive experimental results using four benchmark datasets which we present along with a detailed discussion. [Copyright &y& Elsevier]

Abstract: In this paper we propose a new face recognition method based on the generalized low rank approximations of matrices (GLRAM). First, we investigate the GLRAM and its associated coupled subspace analysis and then propose a new simplified algorithm, which is named as SGLRAM aiming at deriving the projection matrices for GLRAM. We implement all these algorithms (GLRAM SGLRAM) for face recognition on the ORL and YaleB databases and the experiments show that the SGLRAM can produce comparable high performance compared to the approached of two-dimensional principal component analysis (2DPCA) and GLRAM. However, it will cost much less time than the GLRAM in training and save more space than the 2DPCA in testing. [Copyright &y& Elsevier]

For many practical face recognition problems, such as law enforcement, e-passport, ID card identification, and video surveillance, there is usually only a single sample per person enrolled for training, meanwhile the probe samples can usually be captured on the spot, it is possible to collect multiple face images per person. This is a new face recognition problem with many challenges, and we name it as the single-image-to-image-set face recognition problem (ISFR). In this paper, a customized dictionary-based face recognition approach is proposed to solve this problem using the extended joint sparse representation. We first learn a customized variation dictionary from the on-location probing face images, and then propose the extended joint sparse representation, which utilizes the information of both the customized dictionary and the gallery samples, to classify the probe samples. Finally we compare the proposed method with the related methods on several popular face databases, including Yale, AR, CMU-PIE, Georgia, Multi-PIE and LFW databases. The experimental results show that the proposed method outperforms most of these popular face recognition methods for the ISFR problem. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method to tackle the problem of face recognition with occlusions via effectively using colour information. As most of current existing approaches for this problem focus on occlusion detection first as a pre-processing step and then solve recognition problem via occlusion removal, we will develop an effective image representation technique using fused colour information to increase recognition accuracy without occlusion detection. For such purpose, we would first devise a novel local representation for face images because occlusion often appears locally. Technically, we use the recently proposed two dimensional multi-colour fusion (2D-MCF) model in our previous work for image representation, as such model has been proven to be able to achieve very high performance in terms of recognition accuracy. Then, we use the Partitioned sparse sensing recognition (P-SRC) approach for face recognition since this approach is robust for occlusions and does not need any information about the occluded areas as required in most of the existing works. Finally, extensive experiments conducted on four different databases show the superiority of the proposed method over several existing approaches including P-SRC, the Reconstruction based methods and other state of the art methods. The proposed method can improve the face recognition accuracy by up to 24.5%, 3.8%, 25% and 2.86% for the AR database, the Curtin database, the FRGC database and the Busphorus database, respectively. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we propose a new face recognition method based on two-dimensional locality preserving projections (2DLPP) in frequency domain. For this purpose, we first introduce the two-dimensional locality preserving projections. Then the 2DLPP in frequency domain is proposed for face recognition. In fact, two dimensional discrete cosine transform (2DDCT) is used as a pre-processing step and it transforms the face image signals from spatial domain into frequency domain aiming to reduce the effects of illumination and pose changes in face recognition. Then 2DLPP is applied on the upper left corner blocks of the 2DDCT transformed matrices, which represent main energy of each original image. For demonstration, the Olivetti Research Laboratory (ORL), YALE, FERET and YALE-B face datasets are used to compare the proposed approach with the conventional 2DLPP and 2DDCT approaches with the nearest neighborhood (NN) classifier. The experimental results show that the proposed 2DLPP in frequency domain is superior over the 2DLPP in spatial domain and 2DDCT itself in frequency domain. [Copyright &y& Elsevier]