Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

This paper presents a new approach for image set classification, where each training and testing example contains a set of image instances of an object captured from varying viewpoints or under varying illuminations. While a number of image set classification methods have been proposed in recent years, most of them model each image set as a single linear subspace or mixture of linear subspaces, which may lose some discriminative information for classification. To address this, we propose exploring multiple order statistics as features of image sets, and develop a localized multi-kernel metric learning (LMKML) algorithm to effectively combine different order statistics information for classification. Our method achieves the state-of-the-art performance on four widely used databases including the Honda/UCSD, CMU Mobo, and Youtube face datasets, and the ETH-80 object dataset.

/pdf/image-set-classification-using-holistic-multiple-order-1a95f5qnn9.pdf

Image Set Classification Using Holistic Multiple Order Statistics Features and Localized Multi-kernel Metric Learning

This paper presents a new approach to image-set-based face recognition, where each training and testing example is a set of face images captured from varying poses, illuminations, expressions, and resolutions. While a number of image set based face recognition methods have been proposed in recent years, most of them model each face image set as a single linear subspace or as the union of linear subspaces, which may lose some discriminative information for face image set representation. To address this shortcoming, we propose exploiting statistics information as feature representations for face image sets and develop a localized multikernel metric learning algorithm to effectively combine different statistics for recognition. Moreover, we propose a localized multikernel multimetric learning method to jointly learn multiple feature-specific distance metrics in the kernel spaces, one for each statistic feature, to better exploit complementary information for recognition. Our methods achieve state-of-the-art performance on four widely used video face datasets including the Honda, MoBo, YouTube Celebrities, and YouTube Face datasets.

Localized Multifeature Metric Learning for Image-Set-Based Face Recognition

Proposing a proper method for face recognition is still a challenging subject in biometric and computer vision applications. Although some reliable systems were introduced under relatively controlled conditions, their recognition rate is not satisfactory in the general settings. This is especially true when there are variations in pose, illumination, and facial expression. To alleviate these problems, a hybrid face recognition system is proposed which benefits from the superiority of both convolutional neural network (CNN) and support vector machine (SVM). To this end, first a genetic algorithm is employed to find the optimum structure of CNN. Then, the performance of the system is improved by replacing the last layer of CNN with an ensemble of SVMs. Finally, using concepts of error correction, decision is made. The potential of CNN as a trainable feature extractor provides a flexible recognition system that can recognise faces with variations in pose and illumination. Simulation results show that the system achieves good recognition rate and is robust against variations in terms of facial expressions, occlusion, noise, and illuminations.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2015.0037

Genetic algorithm-optimised structure of convolutional neural network for face recognition applications

In this paper, we propose a robust unsupervised algorithm for automatic alignment of two manifolds in different datasets with possibly different dimensionalities. The significant contribution is that the proposed alignment algorithm is performed automatically without any assumptions on the correspondences between the two manifolds. For such purpose, we first automatically extract local feature histograms at each point of the manifolds and establish an initial similarity between the two datasets by matching their histogram-based features. Based on such similarity, an embedding space is estimated where the distance between the two manifolds is minimized while maximally retaining the original structure of the manifolds. The elegance of this idea is that such complicated problem is formulated as a generalized eigenvalue problem, which can be easily solved. The alignment process is achieved by iteratively increasing the sparsity of correspondence matrix until the two manifolds are correctly aligned and consequently one can reveal their joint structure. We demonstrate the effectiveness of our algorithm on different datasets by aligning protein structures, 3D face models and facial images of different subjects under pose and lighting variations. Finally, we also compare with a state-of-the-art algorithm and the results show the superiority of the proposed manifold alignment in terms of vision effect and numerical accuracy.

Unsupervised manifold alignment using soft-assign technique

Face images are usually taken from different camera views with different expressions and illumination. Face recognition based on Image set is expected to achieve better performance than traditional single frame based methods, because this new framework can incorporate information about variations of individual's appearance and make a decision collectively. In this paper we propose a new dimensionality reduction method for image set based face recognition. In the proposed method, we transform each image set into a convex hull and use support vector machine to compute margins between each pair sets. Then we use PCA to do dimension reduction with an aim to preserve those margins. Finally we do classification using a distance based on convex hull in low dimension feature space. Experiments with benchmark face video databases validate the proposed approach.

Margin preserving projection for image set based face recognition

Manifold learning aims to map the original data from a high-dimensional space into a low-dimensional feature space with possible better discriminative structure. In this paper, we propose a supervised manifold learning approach called SubManifold Individuality LEarning (SMILE). In SMILE, the linear subspace derived from the principal component analysis based on data with the same label is named as "the individual subspace", while the linear subspace learned from all data is defined as "the global subspace". For each data sample, the aim of SMILE is to enlarge the diversity between its reconstructed data from individual subspace and global subspace, respectively, so that the intrinsic character of each class can be stimulated in the feature space. SMILE also utilizes the Laplacian matrix to restrict the local structure of data in the low-dimensional feature space in order to preserve the locality of the high-dimensional data. The proposed method is validated in appearance-based face recognition problem on some typical facial image databases via extracting discriminative features. Experimental results show that the proposed approach can obtain the discriminative structure of facial manifold and extract better features for face recognition than other counterparts approaches.

Discriminative structure discovery via dimensionality reduction for facial image manifold

We propose a new unsupervised algorithm for the automatic alignment of two manifolds of different datasets with possibly different dimensionalities. Alignment is performed automatically without any assumptions on the correspondences between the two manifolds. The proposed algorithm automatically establishes an initial set of sparse correspondences between the two datasets by matching their underlying manifold structures. Local feature histograms are extracted at each point of the manifolds and matched using a robust algorithm to find the initial correspondences. Based on these sparse correspondences, an embedding space is estimated where the distance between the two manifolds is minimized while maximally retaining the original structure of the manifolds. The problem is formulated as a generalized eigenvalue problem and solved efficiently. Dense correspondences are then established between the two manifolds and the process is iteratively implemented until the two manifolds are correctly aligned consequently revealing their joint structure. We demonstrate the effectiveness of our algorithm on aligning protein structures, facial images of different subjects under pose variations and RGB and Depth data from Kinect. Comparison with an state-of-the-art algorithm shows the superiority of the proposed manifold alignment algorithm in terms of accuracy and computational time.

Unsupervised iterative manifold alignment via local feature histograms

Average Neighborhood Margin Maximization (ANMM) is an effective method for feature extraction, especially for addressing the Small Sample Size (SSS) problem. For each specific training sample, ANMM enlarges the margin between itself and its neighbors which are not in its class (heterogeneous neighbors), meanwhile keeps this training sample and its neighbors which belong to the same class (homogeneous neighbor) as close as possible. However, these two requirements are sometimes conflicting in practice. For the purpose of balancing these conflicting requirements and discovering the side information for both the homogeneous neighborhood and the heterogeneous neighborhood, we propose a new type of ANMM in this paper, called Balance ANMM (BANMM). The proposed algorithm not only can enhance the discriminative ability of ANMM, but also can preserve the local structure of training data. Experiments conducted on three well-known face databases i.e. Yale, YaleB and CMU PIE demonstrate the proposed algorithm outperforms ANMM in all three data sets.

Feature extraction via balanced average neighborhood margin maximization

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