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Complex Matrix Factorization for Face Recognition
[article]
2016
arXiv
pre-print
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This work developed novel complex matrix factorization methods for face recognition; the methods were complex matrix factorization (CMF), sparse complex matrix factorization (SpaCMF), and graph complex ...
matrix factorization (GraCMF). ...
[1] provided a solution to this problem that was called topology-preserving non-negative matrix factorization (TPNMF). ...
arXiv:1612.02513v1
fatcat:tfw5wghqcrel3ow6beezots5fa
A Hybrid Face Recognition Approach Using GPUMLib
[chapter]
2010
Lecture Notes in Computer Science
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The library includes a high-performance implementation of the Non-Negative Matrix Factorization (NMF) and the Multiple Back-Propagation (MBP) algorithms. ...
Both algorithms are combined in order to obtain a reliable face recognition classifier. ...
Non-Negative Matrix Factorization Given a matrix V ∈ IR n×m + containing only non-negative coefficients (V ij ≥ 0) and a pre-specified positive integer, r < min(n, m), NMF finds two matrices, with non-negative ...
doi:10.1007/978-3-642-16687-7_17
fatcat:m2vgwaqnojddheyura2vngsfge
Commute time guided transformation for feature extraction
2012
Computer Vision and Image Understanding
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., the commute time (CT), to extract visual features for face recognition via a manifold way. ...
Moreover, CTG projections are robust to the graph topology that it can always achieve good recognition performance in spite of different initial graph structures. ...
Berkeley for their constructive suggestions on sparse representation. This work was supported by the National Basic Research Project ( ...
doi:10.1016/j.cviu.2011.11.002
fatcat:spdplrv5ufhonm3f22m2s33tgu
Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition
2014
Journal of Applied Mathematics
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into incremental nonnegative matrix factorization algorithm (INMF); thus, our new algorithm is able to preserve the geometric structure in the data under incremental study framework; secondly, considering ...
close to INMF while the recognition rate outperforms INMF. (3) Comparing with other popular NMF-based face recognition incremental algorithms, our IGNMF and B-IGNMF also outperform then both the recognition ...
Wen, “Topology pre-
serving non-negative matrix factorization for face recognition,”
IEEE Transactions on Image Processing, vol. 17, no. 4, pp. 574–
584, 2008.
[12] X. Liu, S. ...
doi:10.1155/2014/928051
fatcat:kmyensgxdfbwhou3evr3u2iqoe
A Novel Discriminant Non-Negative Matrix Factorization Algorithm With Applications to Facial Image Characterization Problems
2007
IEEE Transactions on Information Forensics and Security
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The methods introduced so far regarding discriminant non-negative matrix factorization (DNMF) do not guarantee convergence to a stationary limit point. ...
The usefulness of the proposed technique to frontal face verification and facial expression recognition problems is demonstrated. ...
Index Terms-Facial expression recognition, frontal face verification, linear discriminant analysis, non-negative matrix factorization (NMF), projected gradients.
I. ...
doi:10.1109/tifs.2007.902017
fatcat:5o3ejhlyxrfy7jdgrvogheqhwa
Graph-Oriented Learning via Automatic Group Sparsity for Data Analysis
2012
2012 IEEE 12th International Conference on Data Mining
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Furthermore, we integrate the proposed graph with several graph-oriented learning algorithms: spectral embedding, spectral clustering, subspace learning and manifold regularized non-negative matrix factorization ...
The newly developed GroupSp-Graph has the same noise-insensitive property as ℓ 1 -Graph, and also can successively preserve the group and local information in the graph. ...
The authors would like to thank the anonymous referees for their helpful comments and suggestions. This research is supported by the National Nature Science Foundation of China (61075043). ...
doi:10.1109/icdm.2012.82
dblp:conf/icdm/FangWD12
fatcat:y5hhme43yvajrat6l25jvjy3qe
Dimensionality Reduction Techniques for Face Recognition
[chapter]
2011
Reviews, Refinements and New Ideas in Face Recognition
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ICA generalizes a widely-used subspace analysis method such as principal component analysis (PCA) and factor analysis, allowing latent variables to be non-Gaussian and basis vectors to be non-orthogonal ...
By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. ...
Dimensionality Reduction Techniques for Face Recognition, Reviews, Refinements and New Ideas in Face Recognition, Dr. ...
doi:10.5772/18251
fatcat:eyppypynmjgfjnixvipskuhzwi
Neighbourhood Discriminant Locally Linear Embedding in Face Recognition
2008
2008 Fifth International Conference on Computer Graphics, Imaging and Visualisation
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Locally Linear Embedding (LLE) is an unsupervised non-linear manifold learning method, which has spurred increased interest in face recognition research recently. ...
Since the locality preservation is correlated to the class discrimination, the proposed cLLE is expected superior to LLE in face recognition. ...
LOCALLY LINEAR EMBEDDING Locally Linear Embedding (LLE) is a non-linear sub-manifold learning algorithm that embeds a high dimensional data into a lower dimensional space while preserving local topological ...
doi:10.1109/cgiv.2008.63
dblp:conf/IEEEcgiv/HanJW08
fatcat:zwvrqkbcqfft3jc4xf6teu3dqa
Robust Expression-Invariant Face Recognition from Partially Missing Data
[chapter]
2006
Lecture Notes in Computer Science
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Here, we apply a new method for measuring isometry-invariant similarity between faces by embedding one facial surface into another. ...
Recent studies on three-dimensional face recognition proposed to model facial expressions as isometries of the facial surface. ...
of non-negative weights. ...
doi:10.1007/11744078_31
fatcat:set2vv6nqvhchdylz5lbc5hqwe
HyperNTF: A Hypergraph Regularized Nonnegative Tensor Factorization for Dimensionality Reduction
[article]
2022
arXiv
pre-print
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A representative algorithm is the Graph Regularized Non-negative Matrix Factorization (GNMF) for image clustering. ...
Thus, we propose a novel method, named Hypergraph Regularized Non-negative Tensor Factorization (HyperNTF), which utilizes hypergraph to encode the complex connections among samples and employs the factor ...
HyperNTF
Modeling of HyperNTF As previous reviewed, non-negative tensor factorization is an effective tool for preserving non-negative property in dimensionality reduction, and hypergraph is an effective ...
arXiv:2101.06827v3
fatcat:qb4u4pfc75a5lk5yit2cp5vu3u
Subspace Image Representation for Facial Expression Analysis and Face Recognition and its Relation to the Human Visual System
[chapter]
2009
Understanding Complex Systems
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Like in Neuroscience, the techniques which perform better for face recognition yield a holistic image representation, while those techniques suitable for facial expression recognition use a sparse or local ...
This is equivalent with embedding constraints in the model design regarding dimensionality reduction, redundant information minimization, mutual information minimization, non-negativity constraints, class ...
Another representation, Local Non negative Matrix Factorization (LNMF) [38] enhances the sparseness of basis images by generating much more sparse, even localized and oriented image features. ...
doi:10.1007/978-3-540-77657-4_14
fatcat:43bstavb4fd6lkwki6ces5l5xe
State of the Art: Face Recognition
[article]
2021
arXiv
pre-print
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This document presents a short review face recognition methods for images with natural and eye occlude faces. ...
The purpose is to select the best baseline approach for solving automatic face recognition of occluded faces. ...
Non-negative matrix factorization (NMF)-based learning provides an effective way for face recognition robust against occlusions. An example is the dictionary learning method proposed by Ou et al. ...
arXiv:2108.11821v1
fatcat:zkmne7gc5vgk5mhopawqhrqciu
Efficient Rank-one Residue Approximation Method for Graph Regularized Non-negative Matrix Factorization
[chapter]
2013
Lecture Notes in Computer Science
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Nonnegative matrix factorization (NMF) aims to decompose a given data matrix X into the product of two lower-rank nonnegative factor matrices U V T . ...
Different from MUR, which updates both factor matrices (U and V ) as a whole in each iteration round, RRA updates each of their columns by approximating the residue matrix by their outer product. ...
Recently, many GNMF variants have been proposed for various applications. Zhang et al. [18] proposed a topology preserving NMF (TPNMF) for face recognition. ...
doi:10.1007/978-3-642-40991-2_16
fatcat:3jlqrzfaczbkbcor7adacwliau
Characterization of Graphs for Protein Structure Modeling and Recognition of Solubility
2016
Current Bioinformatics
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2015 pre-print arXiv:1407.8033v4 fatcat:bkgrghbfqfav7fkpksxuvtog3qOther Versions
We found that protein size is the main discriminator for the solubility, while however there are other factors that help explaining the solubility degree. ...
Results are encouraging and consolidate the potential of pattern recognition techniques when employed to describe complex biological systems. ...
Let us define the normalized Laplacian matrix asL = D −1/2 LD −1/2 . L is symmetric and positive semi-definite, and therefore it has non-negative eigenvalues only. ...
doi:10.2174/1574893611666151109175216
fatcat:ivnousoij5hd7gvct74m43jtou
A Survey of 2D Face Recognition Techniques
2016
Computers
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There are methods that use the entire face as input data for the proposed recognition system, methods that do not consider the whole face, but only some features or areas of the face and methods that use ...
This is because it is inexpensive, non-intrusive and natural. Therefore, researchers have developed dozens of face recognition techniques over the last few years. ...
They are obtained by a rotation followed by a projection. • Non-negative matrix factorization (NMF) [16] : The non-negative matrix factorization is another method that represents the face without using ...
doi:10.3390/computers5040021
fatcat:iilhw5xsv5faflbijltmrkhmgi
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