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Reconstruction-based metric learning for unconstrained face verification,” Information Forensics and Security
- IEEE Transactions on
, 2015
"... Abstract — In this paper, we propose a reconstruction-based metric learning method to learn a discriminative distance metric for unconstrained face verification. Unlike conventional metric learning methods, which only consider the label information of training samples and ignore the reconstruction r ..."
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Abstract — In this paper, we propose a reconstruction-based metric learning method to learn a discriminative distance metric for unconstrained face verification. Unlike conventional metric learning methods, which only consider the label information of training samples and ignore the reconstruction residual infor-mation in the learning procedure, we apply a reconstruction criterion to learn a discriminative distance metric. For each training example, the distance metric is learned by enforcing a margin between the interclass sparse reconstruction residual and interclass sparse reconstruction residual, so that the recon-struction residual of training samples can be effectively exploited to compute the between-class and within-class variations. To better use multiple features for distance metric learning, we propose a reconstruction-based multimetric learning method to collaboratively learn multiple distance metrics, one for each feature descriptor, to remove uncorrelated information for recog-nition. We evaluate our proposed methods on the Labelled Faces in the Wild (LFW) and YouTube face data sets and our experimental results clearly show the superiority of our methods over both previous metric learning methods and several state-of-the-art unconstrained face verification methods. Index Terms — Face recognition, unconstrained face verification, metric learning, reconstruction-based learning.
Deeply coupled auto-encoder networks for cross-view classification. arXiv preprint arXiv:1402.2031
, 2014
"... The comparison of heterogeneous samples extensively exists in many applications, especially in the task of im-age classification. In this paper, we propose a simple but effective coupled neural network, called Deeply Coupled Autoencoder Networks (DCAN), which seeks to build two deep neural networks, ..."
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The comparison of heterogeneous samples extensively exists in many applications, especially in the task of im-age classification. In this paper, we propose a simple but effective coupled neural network, called Deeply Coupled Autoencoder Networks (DCAN), which seeks to build two deep neural networks, coupled with each other in ev-ery corresponding layers. In DCAN, each deep structure is developed via stacking multiple discriminative cou-pled auto-encoders, a denoising auto-encoder trained with maximum margin criterion consisting of intra-class com-pactness and inter-class penalty. This single layer com-ponent makes our model simultaneously preserve the lo-cal consistency and enhance its discriminative capabil-ity. With increasing number of layers, the coupled net-works can gradually narrow the gap between the two views. Extensive experiments on cross-view image clas-sification tasks demonstrate the superiority of our method over state-of-the-art methods. 1
Discriminative Shared Gaussian Processes for Multiview and View-Invariant Facial Expression Recognition
"... Abstract — Images of facial expressions are often captured from various views as a result of either head movements or variable camera position. Existing methods for multiview and/or view-invariant facial expression recognition typically perform classification of the observed expression using either ..."
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Abstract — Images of facial expressions are often captured from various views as a result of either head movements or variable camera position. Existing methods for multiview and/or view-invariant facial expression recognition typically perform classification of the observed expression using either classifiers learned separately for each view or a single classifier learned for all views. However, these approaches ignore the fact that different views of a facial expression are just different manifestations of the same facial expression. By accounting for this redundancy, we can design more effective classifiers for the target task. To this end, we propose a discriminative shared Gaussian process latent variable model (DS-GPLVM) for multiview and view-invariant classification of facial expressions from multiple views. In this model, we first learn a discriminative manifold shared by multiple views of a facial expression. Subsequently, we perform facial expression classification in the expression manifold. Finally, classification of an observed facial expression is carried out either in the view-invariant manner (using only a single view of the expression) or in the multiview manner (using multiple views of the expression). The proposed model can also be used to perform fusion of different facial features in a principled manner. We validate the proposed DS-GPLVM on both posed and spontaneously displayed facial expressions from three publicly available datasets (MultiPIE, labeled face parts in the wild, and static facial expressions in the wild). We show that this model outperforms the state-of-the-art methods for multiview and view-invariant facial expression classification, and several state-of-the-art methods for multiview learning and feature fusion. Index Terms — View-invariant, multi-view learning, facial expression recognition, Gaussian Processes.
Cluster Canonical Correlation Analysis
"... In this paper we present cluster canonical cor-relation analysis (cluster-CCA) for joint dimen-sionality reduction of two sets of data points. Unlike the standard pairwise correspondence be-tween the data points, in our problem each set is partitioned into multiple clusters or classes, where the cla ..."
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In this paper we present cluster canonical cor-relation analysis (cluster-CCA) for joint dimen-sionality reduction of two sets of data points. Unlike the standard pairwise correspondence be-tween the data points, in our problem each set is partitioned into multiple clusters or classes, where the class labels define correspondences be-tween the sets. Cluster-CCA is able to learn dis-criminant low dimensional representations that maximize the correlation between the two sets while segregating the different classes on the learned space. Furthermore, we present a kernel extension, kernel cluster canonical correlation analysis (cluster-KCCA) that extends cluster-CCA to account for non-linear relationships. Cluster-(K)CCA is shown to be computationally efficient, the complexity being similar to stan-dard (K)CCA. By means of experimental evalu-ation on benchmark datasets, cluster-(K)CCA is shown to achieve state of the art performance for cross-modal retrieval tasks. 1
Cross-Modality 2D-3D Face Recognition via Multiview Smooth Discriminant Analysis Based on ELM
"... In recent years, 3D face recognition has attracted increasing attention from worldwide researchers. Rather than homogeneous face data, more and more applications require flexible input face data nowadays. In this paper, we propose a new approach for cross-modality 2D-3D face recognition (FR), which ..."
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In recent years, 3D face recognition has attracted increasing attention from worldwide researchers. Rather than homogeneous face data, more and more applications require flexible input face data nowadays. In this paper, we propose a new approach for cross-modality 2D-3D face recognition (FR), which is called Multiview Smooth Discriminant Analysis (MSDA) based on Extreme Learning Machines (ELM). Adding the Laplacian penalty constrain for the multiview feature learning, the proposed MSDA is first proposed to extract the cross-modality 2D-3D face features. The MSDA aims at finding a multiview learning based common discriminative feature space and it can then fully utilize the underlying relationship of features from different views. To speed up the learning phase of the classifier, the recent popular algorithm named Extreme Learning Machine (ELM) is adopted to train the single hidden layer feedforward neural networks (SLFNs). To evaluate the effectiveness of our proposed FR framework, experimental results on a benchmark face recognition dataset are presented. Simulations show that our new proposed method generally outperforms several recent approaches with a fast training speed.
Multi-view Discriminant Analysis with Tensor Representation and Its Application to Cross-view Gait Recognition
"... Abstract-This paper describes a method of discriminant analysis for cross-view recognition with a relatively small number of training samples. Since appearance of a recognition target (e.g., face, gait, gesture, and action) is in general drastically changes as an observation view changes, we introd ..."
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Abstract-This paper describes a method of discriminant analysis for cross-view recognition with a relatively small number of training samples. Since appearance of a recognition target (e.g., face, gait, gesture, and action) is in general drastically changes as an observation view changes, we introduce multiple view-specific projection matrices and consider to project a recognition target from a certain view by a corresponding view-specific projection matrix into a common discriminant subspace. Moreover, conventional vectorized representation of an originally higher-order tensor object (e.g., a spatio-temporal image in gait recognition) often suffers from the curse of dimensionality dilemma, we therefore encapsulate the multiple view-specific projection matrices in a framework of discriminant analysis with tensor representation, which enables us to overcome the curse of dimensionality dilemma. Experiments of cross-view gait recognition with two publicly available gait databases show the effectiveness of the proposed method in case where a training sample size is small.
Cross-View Gait Recognition Using View-Dependent Discriminative Analysis
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Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence A Tree-Based Tabu Search Algorithm for the Manpower Allocation Problem with Time Windows and Job-Teaming Constraints
"... This paper investigates the manpower allocation problem with time windows and job-teaming constraints (MAPTWTC), a practical scheduling and routing problem that tries to synchronize workers’ schedules to complete all tasks. We first provide an integer programming model for the problem and discuss it ..."
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This paper investigates the manpower allocation problem with time windows and job-teaming constraints (MAPTWTC), a practical scheduling and routing problem that tries to synchronize workers’ schedules to complete all tasks. We first provide an integer programming model for the problem and discuss its properties. Next, we show that tree data structure can be used to represent the MAPTWTC solutions, and its optimal solution can be obtained from one of trees by solving a minimum cost flow model for each worker type. Consequently, we develop for the problem a novel tabu search algorithm employing search operators based on the tree data structure. Finally, we prove the effectiveness of the tabu search algorithm by computational experiments on two sets of instances. 1
Rank Consistency based Multi-View Learning: A Privacy-Preserving Approach
"... Complex media objects are often described by multi-view feature groups collected from diverse domains or information channels. Multi-view learning, which attempts to exploit the relationship am-ong multiple views to improve learning performance, has drawn extensive attention. It is noteworthy that i ..."
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Complex media objects are often described by multi-view feature groups collected from diverse domains or information channels. Multi-view learning, which attempts to exploit the relationship am-ong multiple views to improve learning performance, has drawn extensive attention. It is noteworthy that in some real-world appli-cations, features of different views may come from different pri-vate data repositories, and thus, it is desired to exploit view re-lationship with data privacy preserved simultaneously. Existing multi-view learning approaches such as subspace methods and pre-fusion methods are not applicable in this scenario because they need to access the whole features, whereas late-fusion approaches could not exploit information from other views to improve the in-dividual view-specific learners. In this paper, we propose a novel multi-view learning framework which works in a hybrid fusion manner. Specifically, we convert predicted values of each view into an Accumulated Prediction Matrix (APM) with low-rank con-straint enforced jointly by the multiple views. The joint low-rank constraint enables the view-specific learner to exploit other views to help improve the performance, without accessing the features of other views. Thus, the proposed RANC framework provides a privacy-preserving way for multi-view learning. Furthermore, we consider variants of solutions to achieve rank consistency and present corresponding methods for the optimization. Empirical in-vestigations on real datasets show that the proposed method achiev-es state-of-the-art performance on various tasks.
Learning Compact Binary Face Descriptor for Face Recognition
"... Abstract—Binary feature descriptors such as local binary patterns (LBP) and its variations have been widely used in many face recognition systems due to their excellent robustness and strong discriminative power. However, most existing binary face descriptors are hand-crafted, which require strong p ..."
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Abstract—Binary feature descriptors such as local binary patterns (LBP) and its variations have been widely used in many face recognition systems due to their excellent robustness and strong discriminative power. However, most existing binary face descriptors are hand-crafted, which require strong prior knowledge to engineer them by hand. In this paper, we propose a compact binary face descriptor (CBFD) feature learning method for face representation and recognition. Given each face image, we first extract pixel difference vectors (PDVs) in local patches by computing the difference between each pixel and its neighboring pixels. Then, we learn a feature mapping to project these pixel difference vectors into low-dimensional binary vectors in an unsupervised manner, where 1) the variance of all binary codes in the training set is maximized, 2) the loss between the original real-valued codes and the learned binary codes is minimized, and 3) binary codes evenly distribute at each learned bin, so that the redundancy information in PDVs is removed and compact binary codes are obtained. Lastly, we cluster and pool these binary codes into a histogram feature as the final representation for each face image. Moreover, we propose a coupled CBFD (C-CBFD) method by reducing the modality gap of heterogeneous faces at the feature level to make our method applicable to heterogeneous face recognition. Extensive experimental results on five widely used face datasets show that our methods outperform state-of-the-art face descriptors. Index Terms—Face recognition, heterogeneous face matching, feature learning, binary feature, compact feature, biometrics Ç 1
