Cross-domain face matching between the thermal and visible spectrum is a desired technology to recognize the visible face image with probe images captured in the thermal spectrum for the night-time surveillance and security applications. However, the large modality gap between faces captured in different spectrum makes thermal-to-visible face recognition quite a challenging problem. Classical Independent Components Analysis (ICA) and its variants have been successfully used for feature learning, and the projection matrix can be used for forming the basis of images. In this paper, we present a deep joint independent component analysis network (DJICAN), a deep architecture based on multilayer independent component analysis, which learns the mutual mappings between visible and thermal face images. Joint ICA assumes the sources are the same for a visible image and its corresponding thermal image. The goal of DJICAN is to obtain the basis matrices of visible and thermal face images, which represent the individual imaging systems for the two domains. First, a forward multilayer ICA is performed. Then, we use a novel backpropagation algorithm based on a reconstruction loss function to optimize the ICA basis and the sources. Extensive experiments are performed on the ARL polarimetric thermal facial datasets that contain face images that have been taken at three different ranges and with different face expressions. The results show that the proposed method performs better than the state-of-the-art methods in terms of both synthetic image quality and thermal-to-visible face recognition accuracy.
Heterogeneous face recognition (HFR) refers to matching face imagery across different domains, such as identifying a thermal probe image given a gallery of visible face images. In this paper, we propose a method for thermal to visible face recognition based on coupled independent component analysis (Coupled ICA). It has been reported that independent component analysis (ICA) of natural scene patches produces a set of visual filters that resemble the receptive fields of simple cells in visual cortex and the projection matrix form a basis of images. Aiming to learn a common latent space for cross-modal images, we propose to learn a separate set of ICA filters which represent the respective imaging system in each domain using a coupled architecture. Coupled ICA assumes the image sources from one domain to be identical to those observed at the other domain. Pairs of image patches in the two domains jointly update the projection matrix in Coupled ICA model. The obtained ICA filters are used to transform images into a domain-independent latent space via patch-wise synthesis. In addition, we add cross-examples into a one-vs-all sparse representation (SR) classification strategy to improve classification performance. Experiments were conducted with the ARL Multi-modal Face Dataset. The results show that the proposed method can fuse the thermal and visible images and outperforms the state-of-the-art methods of cross-modal face recognition.
Cross-model heterogeneous face recognition (HFR) has been one of the most challenging areas of research in biometrics and computer vision. The main goal of HFR is to accurately recognize/identify the visible face image with probe images captured in alternative sensing modalities, such as thermal spectrum. The polarization state information of thermal faces contains the missing textural and geometrics details in the conventional thermal face imagery, which facilitate the development of cross-spectrum face recognition. In this paper, we propose a coupled dictionary learning architecture to find a common embedding space between the visible and sensing domains, where we model the learning problem as a bilevel optimization. The learned coupled dictionaries are used to transform visible and polarimetric thermal face images into the common embedding feature space via patch-wise sparse recovery. Experiments conducted with the polarimetric thermal facial datasets which contains face images that has been taken at three different ranges and with different face expressions. The results show that our proposed coupled learning method could fuse polarimetric and thermal features in a way to outperform the conventional methods and enhance the performance of a thermal-to-visible face recognition system.
Sparse representation classification (SRC) is being widely applied for target detection in hyperspectral images (HSI). However, due to the problem of the curse of dimensionality and redundant information in HSI, SRC methods fail to achieve high classification performance via a large number of spectral bands. Selecting a subset of predictive features in a high-dimensional space is a challenging problem for hyperspectral image classification. In this paper, we propose a novel discriminant feature selection (DFS) method for hyperspectral image classification in the eigenspace. Firstly, our proposed DFS method selects a subset of discriminant features by solving the combination of spectral and spatial hypergraph Laplacian quadratic problem, which can preserve the intrinsic structure of the unlabeled pixels as well as both the inter-class and intra-class constraints defined on the labeled pixels in the projected low-dimensional eigenspace. Then, in order to further improve the classification performance of SRC, we exploit the well-known simultaneous orthogonal matching pursuit (SOMP) algorithm to obtain the sparse representation of the pixels by incorporating the interpixel correlation within the classical OMP by assuming that neighboring pixels usually consist of similar materials. Finally, the recovered sparse errors are directly used for determining the label of the pixels. The extracted discriminant features are compatibly used in conjunction with the established SRC methods, and can significantly improve their performance for HSI classification. Experiments conducted with the hyperspectral data sets and different experimental settings show that our proposed method increases the classification accuracy and outperforms the state-of-the-art feature selection and classification methods.
Sparse Representation (SR) has received an increasing amount of interest in recent years. It aims to find the sparsest representation of each data capturing high-level semantics among the linear combinations of the base sets in a given dictionary. In order to further improve the classification performance, the joint SR that incorporates interpixel correlation information of neighborhoods has been proposed for image pixel classification. However, joint SR method yields high computational cost. To improve the performance and computation efficiency of SR and joint SR, we propose a seeded Laplacian based on sparse representation (SeedLSR) framework for hyperspectral image classification, where a hypergraph Laplacian explicitly takes into account the local manifold structure of the hyperspectral pixel in a spatial-type weighted graph. Given the training data in a dictionary, SeedLSR algorithm firstly finds the sparse representation of hyperspectral pixels, which is used to define the spectral-type affinity matrix of an undirected graph. Then, using the training data as user-defined seeds, the final classification can be obtained by solving the combination of spectral and spatial hypergraph Laplacian quadratic problem. To assess the efficiency of the proposed SeedLSR method, experiments were performed on the scene data under daylight illumination. Compared with SR algorithm, the classification results vary smoothly along the geodesics of the data manifold.
Sparse Representation (SR) is an effective classification method. Given a set of data vectors, SR aims at finding the sparsest representation of each data vector among the linear combinations of the bases in a given dictionary. In order to further improve the classification performance, the joint SR that incorporates interpixel correlation information of neighborhoods has been proposed for image pixel classification. However, SR and joint SR demand significant amount of computational time and memory, especially when classifying a large number of pixels. To address this issue, we propose a superpixel sparse representation (SSR) algorithm for target detection in hyperspectral imagery. We firstly cluster hyperspectral pixels into nearly uniform hyperspectral superpixels using our proposed patch-based SLIC approach based on their spectral and spatial information. The sparse representations of these superpixels are then obtained by simultaneously decomposing superpixels over a given dictionary consisting of both target and background pixels. The class of a hyperspectral pixel is determined by a competition between its projections on target and background subdictionaries. One key advantage of the proposed superpixel representation algorithm with respect to pixelwise and joint sparse representation algorithms is that it reduces computational cost while still maintaining competitive classification performance. We demonstrate the effectiveness of the proposed SSR algorithm through experiments on target detection in the in-door and out-door scene data under daylight illumination as well as the remote sensing data. Experimental results show that SSR generally outperforms state of the art algorithms both quantitatively and qualitatively.
Probabilistic atlas based on human anatomical structure has been widely used for organ segmentation. The challenge is how to register the probabilistic atlas to the patient volume. Additionally, there is the disadvantage that the conventional probabilistic atlas may cause a bias toward the specific patient study due to a single reference. Hence, we propose a template matching framework based on an iterative probabilistic atlas for organ segmentation. Firstly, we find a bounding box for the organ based on human anatomical localization. Then, the probabilistic atlas is used as a template to find the organ in this bounding box by using template matching technology. Comparing our method with conventional and recently developed atlas-based methods, our results show an improvement in the segmentation accuracy for multiple organs (p < 0:00001).
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