Application of non-negative and local non negative matrix factorization to facial expression recognition

Buciu, Ioan; Pitas, Ioannis

doi:10.1109/icpr.2004.1334109

Cited by 84 publications

(44 citation statements)

References 14 publications

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“…in ranging from biology (Sotiras et al, 2015, Brunet et al, 2004, nuclear sciences or in to computer sciences (e. g. signal processing and pattern recognition; Smaragdis et al, 2003, Buciu et al, 2004). Devarajan's work focuses on the field of computational biology, however it also gives a remarkable outlook to the capabilities of NMF-analysis (Devarajan, 2008).…”

Section: Non-negative Matrix Factorization In Briefmentioning

confidence: 99%

Determining Projections of Grain Boundaries from Diffraction Data in Transmission Electron Microscope

Kiss

Lábár

2016

Microsc Microanal

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Keywords: grain boundary plane, non-negative matrix factorization, electron diffraction, computer program, polycrystalline thin films Microscopy and Microanalysis, a Cambridge University Press journal AbstractGrain boundaries (GB) are characterized by disorientation of the neighboring grains and the direction of the boundary plane between them. A new approach presented here determines the projection of grain boundaries that can be used to determine the latter one. The novelty is that an additional parameter of GB-s is quantified in addition to the ones provided by the orientation maps, namely the width of the projection of the GB is measured from the same set of diffraction patterns that were recorded for the orientation map, without the need to take any additional images. The diffraction patterns are collected in nanobeam diffraction (NBD) mode in a transmission electron microscope (TEM) pixel-by-pixel from an area containing two neighboring grains and the boundary between them. In our case the diffraction patterns were recorded using the beam scanning function of the a commercially available ASTAR system (ASTAR). Our method is based on non-negative matrix factorization (NMF) applied to the mentioned set of diffraction patterns. The method is encoded in a MATLAB environment, making the results easy to interpret and visualize. The measured GB-projection width is used to determine the orientation of the grain boundary plane, as given in Microsc. Microanal. 21, 422-435Kiss et al., 2015.

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Section: Non-negative Matrix Factorization In Briefmentioning

confidence: 99%

Determining Projections of Grain Boundaries from Diffraction Data in Transmission Electron Microscope

Kiss

Lábár

2016

Microsc Microanal

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“…Recognition accuracy 81.4% on Cohn-Kanade (CK) [5] dataset reported by Buciu et al [1]. They used Principal Component Analysis (PCA) as baseline and proposed a nonnegative matrix factorization and a local non-negative matrix factorization technique for recognizing six facial expressions.…”

Section: Related Workmentioning

confidence: 99%

From Emotion Recognition to Website Customizations

Efremides¹

2016

ijacsa

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Abstract-A computer vision system that recognizes the emotions of a website's user and customizes the context and the presentation of this website accordingly is presented herein. A logistic regression classifiers is trained over the Extended CohnKanade dataset in order to recognize the emotions. The ScaleInvariant Feature Transform algorithm over two different part of an image, the face and the eyes without any special pixel intensities preprocessing, is used to describe each emotion. The testing phase shows a significant improvement in the classification results. A toy web site, as a proof of concept, is also developed.

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“…Buciu and Pitas employed NMF for FER by enforcing local constraints to create a local NMF (LMF) model [22]. Nikitidis et al incorporated discriminant constraints to build the supervised NMF learning method [23,24].…”

Section: Introductionmentioning

confidence: 99%

Projective complex matrix factorization for facial expression recognition

Duong

Lee

Ding

et al. 2018

EURASIP J. Adv. Signal Process.

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In this paper, a dimensionality reduction method applied on facial expression recognition is investigated. An unsupervised learning framework, projective complex matrix factorization (proCMF), is introduced to project high-dimensional input facial images into a lower dimension subspace. The proCMF model is related to both the conventional projective nonnegative matrix factorization (proNMF) and the cosine dissimilarity metric in the simple manner by transforming real data into the complex domain. A projective matrix is then found through solving an unconstraint complex optimization problem. The gradient descent method was utilized to optimize a complex cost function. Extensive experiments carried on the extended Cohn-Kanade and the JAFFE databases show that the proposed proCMF model provides even better performance than state-of-the-art methods for facial expression recognition.

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Application of non-negative and local non negative matrix factorization to facial expression recognition

Cited by 84 publications

References 14 publications

Determining Projections of Grain Boundaries from Diffraction Data in Transmission Electron Microscope

Determining Projections of Grain Boundaries from Diffraction Data in Transmission Electron Microscope

From Emotion Recognition to Website Customizations

Projective complex matrix factorization for facial expression recognition

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