Nuclear norm-based 2-DPCA for extracting features from images
The 2-D principal component analysis (2-DPCA) is a widely used method for image feature
extraction. However, it can be equivalently implemented via image-row-based principal …
extraction. However, it can be equivalently implemented via image-row-based principal …
Illumination invariant face recognition using convolutional neural networks
NP Ramaiah, EP Ijjina… - 2015 IEEE International …, 2015 - ieeexplore.ieee.org
Face is one of the most widely used biometric in security systems. Despite its wide usage,
face recognition is not a fully solved problem due to the challenges associated with varying …
face recognition is not a fully solved problem due to the challenges associated with varying …
Face recognition using gabor wavelet features with pca and kpca-a comparative study
A Vinay, VS Shekhar, KNB Murthy… - Procedia Computer …, 2015 - Elsevier
Face recognition (FR) is one of the most prominentforms of biometric recognitionthat
proffersa myriad ofcross-domain applications andaugmenting its proficiency has been on …
proffersa myriad ofcross-domain applications andaugmenting its proficiency has been on …
Learning from normalized local and global discriminative information for semi-supervised regression and dimensionality reduction
Semi-supervised dimensionality reduction is one of the important topics in pattern
recognition and machine learning. During the past decade, Laplacian Regularized Least …
recognition and machine learning. During the past decade, Laplacian Regularized Least …
Kernel flexible manifold embedding for pattern classification
Y El Traboulsi, F Dornaika, A Assoum - Neurocomputing, 2015 - Elsevier
Abstract Flexible Manifold Embedding (FME) has been recently proposed as a semi-
supervised graph-based label propagation method. It aims at estimating simultaneously the …
supervised graph-based label propagation method. It aims at estimating simultaneously the …
Kernel low-rank representation for face recognition
H Nguyen, W Yang, F Shen, C Sun - Neurocomputing, 2015 - Elsevier
Face recognition is one of the fundamental problems of computer vision and pattern
recognition. Based on the recent success of Low-Rank Representation (LRR), we propose a …
recognition. Based on the recent success of Low-Rank Representation (LRR), we propose a …
Semi-supervised image classification by nonnegative sparse neighborhood propagation
This paper proposes an enhanced semi-supervised classification approach termed
Nonnegative Sparse Neighborhood Propagation (SparseNP) that is an improvement to the …
Nonnegative Sparse Neighborhood Propagation (SparseNP) that is an improvement to the …
Dimensionality reduction by integrating sparse representation and Fisher criterion and its applications
Sparse representation shows impressive results for image classification, however, it cannot
well characterize the discriminant structure of data, which is important for classification. This …
well characterize the discriminant structure of data, which is important for classification. This …
MR image super-resolution reconstruction using sparse representation, nonlocal similarity and sparse derivative prior
D Zhang, J He, Y Zhao, M Du - Computers in biology and medicine, 2015 - Elsevier
In magnetic resonance (MR) imaging, image spatial resolution is determined by various
instrumental limitations and physical considerations. This paper presents a new algorithm …
instrumental limitations and physical considerations. This paper presents a new algorithm …
Face recognition based on deep neural network
L Xinhua, Y Qian - International Journal of Signal Processing, Image …, 2015 - earticle.net
In modern life, we see more techniques of biometric features recognition have been used to
our surrounding life, especially the applications in telephones and laptops. These biometric …
our surrounding life, especially the applications in telephones and laptops. These biometric …