Robust Kernel Representation With Statistical Local Features for Face Recognition

Yang, Meng; Zhang, Lei; Shiu, Simon; Zhang, David

doi:10.1109/tnnls.2013.2245340

Cited by 113 publications

(62 citation statements)

References 60 publications

(77 reference statements)

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“…This is probably why progress in face identification has been relatively insignificant over the last five years. Though the proposed face identification approaches have become increasingly complex, recognition performance according to the benchmark evaluations remained relatively constant (Xie et al, 2010;Yang et al, 2013;Cament et al, 2014;Chai et al, 2014). To make a breakthrough in face identification, it seems we must revisit the foundation of face recognition, and have a fresh look at the fundamental building blocks of face recognition.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Gabor Phase for Face Identification in Controlled Scenarios

Yang

Liu

2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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Abstract:Gabor features have been widely employed in solving face recognition problems in controlled scenarios. To construct discriminative face features from the complex Gabor space, the amplitude information is commonly preferred, while the other one -the phase -is not well utilized due to its spatial shift sensitivity. In this paper, we address the problem of face recognition in controlled scenarios. Our focus is on the selection of a suitable signal representation and the development of a better strategy for face feature construction. We demonstrate that through our Block Matching scheme Gabor phase information is powerful enough to improve the performance of face identification. Compared to state of the art Gabor filtering based approaches, the proposed algorithm features much lower algorithmic complexity. This is mainly due to our Block Matching enables the employment of high definition Gabor phase. Thus, a single-scale Gabor frequency band is sufficient for discrimination. Furthermore, learning process is not involved in the facial feature construction, which avoids the risk of building a database-dependent algorithm. Benchmark evaluations show that the proposed learning-free algorithm outperforms state-of-the-art Gabor approaches and is even comparable to Deep Learning solutions.

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Section: Introductionmentioning

confidence: 99%

Leveraging Gabor Phase for Face Identification in Controlled Scenarios

Yang

Liu

2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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“…Hybrid architectures are formed to overcome limitations of individual system. The power in such systems stem from their capabilities to exhibit multiple information processing [1,2,3,12] Recent developments in the area of image recognition involve methods for extraction, classification, and selections [4,13,14]. An open problem in this area is to find the best features that enable success recognition in classification processes.…”

Section: Introductionmentioning

confidence: 99%

Pattern Visualization Through Detection Plane Generation for Macroscopic Imagery

Adlan¹

2016

ijacsa

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Abstract-Macroscopic images are kind of environments in which complex patterns are present. Satellite images are one of these classes where many patterns are present. This fact reflects the challenges in detecting patterns present in this kind of environments. SPOT1b satellite images provide valuable information. These images are affordable and can be applicable in wide applications. This paper demonstrates an approach to generate detection plane that visualize patterns present in the satellite image. The detection plane uses rough neural network to provide optimal representation in backpropagation architecture. Rough set theory combined with multilayer perceptron constitutes the rough neural network. Reduction in the feature dimensionality via the rough module improves the recognition ability of the neural network. It is found that the rough module provides the neural network with optimal features. The ability of the neural network to efficiently detect and visualize the pattern stems from a developed extraction algorithm. The result of the hybrid architecture provides the plane with the best features that visualize the phenomena under investigation. Together with the novel extraction algorithm, the developed system provides a tool to visualize patterns present in SPOT1b Satellite image.

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“…A classical way to deal with this is to adopt the "kernel trick " [25], which maps the features into high dimensional feature space to make features of different categories more linearly separable. In this case, we may find a sparse representation for the features more easily [5,41]. With the introduction of kernel techniques, the learned dictionary becomes versatile.…”

Section: Introductionmentioning

confidence: 99%

Self-explanatory Sparse Representation for Image Classification

Liu

Wang

Shen

et al. 2014

Computer Vision – ECCV 2014

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Abstract. Traditional sparse representation algorithms usually operate in a single Euclidean space. This paper leverages a self-explanatory reformulation of sparse representation, i.e., linking the learned dictionary atoms with the original feature spaces explicitly, to extend simultaneous dictionary learning and sparse coding into reproducing kernel Hilbert spaces (RKHS). The resulting single-view self-explanatory sparse representation (SSSR) is applicable to an arbitrary kernel space and has the nice property that the derivatives with respect to parameters of the coding are independent of the chosen kernel. With SSSR, multiple-view self-explanatory sparse representation (MSSR) is proposed to capture and combine various salient regions and structures from different kernel spaces. This is equivalent to learning a nonlinear structured dictionary, whose complexity is reduced by learning a set of smaller dictionary blocks via SSSR. SSSR and MSSR are then incorporated into a spatial pyramid matching framework and developed for image classification. Extensive experimental results on four benchmark datasets, including UIUC-Sports, Scene 15, Caltech-101, and Caltech-256, demonstrate the effectiveness of our proposed algorithm.

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Robust Kernel Representation With Statistical Local Features for Face Recognition

Cited by 113 publications

References 60 publications

Leveraging Gabor Phase for Face Identification in Controlled Scenarios

Leveraging Gabor Phase for Face Identification in Controlled Scenarios

Pattern Visualization Through Detection Plane Generation for Macroscopic Imagery

Self-explanatory Sparse Representation for Image Classification

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