Face Aging Effect Simulation using Hidden Factor Analysis Joint Sparse Representation.

In this paper, we present a novel approach to such an issue by using hidden factor analysis joint sparse representation. ... Face aging simulation has received rising investigations nowadays, whereas it still remains a challenge to generate convincing and natural age-progressed face images. ... CONCLUSION In this paper, we present a novel approach to face aging effect simulation using Hidden Factor Analysis joint sparse representation. ...

doi:10.1109/tip.2016.2547587 pmid:27093721 fatcat:cxlrenbkt5cbffcfjo6kvvoowi

Multiple Versions

Moreover, the aging databases used in most methods to learn the aging process are also reviewed. ... Face Aging has raised considerable attentions and interest from the computer vision community in recent years. ... [40] represented person-specific and age-specific factors independently using sparse representation hidden factor analysis (HFA). ...

arXiv:1802.08726v1 fatcat:vomix3nu6nfsdmnbfhv6ivjjeu

Analysis of hidden unit activations indicated that the age of acquisition effect reflects a gradual reduction in network plasticity and a consequent failure to differentiate late items as effectively as ... Further simulations examined the effects of vocabulary size, learning rate, sparseness of coding, use of a modified learning algorithm, loss of early items, acquisition of very late items, and lesioning ... Age of Acquisition and Pattern Differentiation at the Hidden Units (Simulation 6) Two sets of 50 input-output patterns (A and B) were used for this analysis. ...

doi:10.1037//0278-7393.26.5.1103 pmid:11009247 fatcat:fpzi2opnsbcpbp7hmg3q6eug24

Analysis of hidden unit activations indicated that the age of acquisition effect reflects a gradual reduction in network plasticity and a consequent failure to differentiate late items as effectively as ... Further simulations examined the effects of vocabulary size, learning rate, sparseness of coding, use of a modified learning algorithm, loss of early items, acquisition of very late items, and lesioning ... Age of Acquisition and Pattern Differentiation at the Hidden Units (Simulation 6) Two sets of 50 input-output patterns (A and B) were used for this analysis. ...

doi:10.1037/0278-7393.26.5.1103 fatcat:yzroatmsdba7jga2nuvirk4tmy

Face recognition technology based on deep learning has been widely used in various fields such as finance, education, security, transportation, and new retail. ... learning—face recognition methods based on convolutional neural networks and face recognition methods based on deep belief networks. ... With the development of sparse representation, John Wright et al. proposed to apply sparse representation to face recognition, and mentioned that if sparse representation is correctly applied to face recognition ...

doi:10.54097/hset.v24i.3921 fatcat:uhqbsnzxnjf35i56iift5ux4qq

Open Access

Hence, the otherrace effect affects face-based age estimation systems. ... Age estimation is an automated method of predicting human age from 2-D facial feature representations. ... In this work, face images were classified into eight (8) age groups and principal component analysis was used for appearance and texture information representation. ...

doi:10.14569/ijacsa.2021.0121124 fatcat:mafgxuzpwfer3ehonpuw5pwevu

Szczepanski

For this reason, different feature extraction methods, such as time-domain, frequency-domain and time-frequency-domain methods, have always been used to extract features, and they can be used to generate ... Two fault diagnosis experiments on self-confirmation sensor and rolling bearing shown the robustness and effectiveness of this proposed method. ... Final results of these two simulation experiments can be used to verify the effectiveness of this proposed method. A. ...

doi:10.1109/access.2020.2978112 fatcat:sqmmcz4rbzeyvkhtta7f2ujtvq

DOAJ

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition ... Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. ... The so-called face recognition is to use features of computer analysis, video or image and extract the effective identification information, finally discriminant face the identity of the object. ...

doi:10.5370/jeet.2017.12.1.363 fatcat:xrgzmayk3vgsrovcnucjjhngau

In current paper, we enhanced that architecture (i) by using a loss function adapted to input data with missing values, and (ii) by incorporating side information. ... Experiments presented in this paper were carried out using Grid'5000 testbed, hosted by Inria and supported by CNRS, RENATER and several Universities as well as other organizations. ... However, these methods are linear and cannot catch subtle factors. Newer algorithms were explored to face those constraints such as Factorization Machines [25] . ...

doi:10.1145/2988450.2988456 dblp:conf/recsys/StrubGM16 fatcat:6kpaxy2phra7zb57gt4nwts7rm

Multiple Versions

In this paper, we introduce a Collaborative Filtering Neural network architecture aka CFN which computes a non-linear Matrix Factorization from sparse rating inputs and side information. ... Such algorithms look for latent variables in a large sparse matrix of ratings. They can be enhanced by adding side information to tackle the well-known cold start problem. ... Experiments were carried out using Grid'5000 tested, supported by Inria, CNRS, RENATER and several universities as well as other organizations. ...

arXiv:1603.00806v3 fatcat:5wkbldkz6zc7vmndzf5vewy4xy

Multiple Versions

In case of multiple heterogeneous data sources, a matrix trifactorization technique can successfully fuse all the information in a joint model. ... In presence of noise and sparse data, our data integration method outperform other techniques, both in the synthetic and in the AML data. ... We are also grateful to Marco Piastra, Gianluca Gerard and Stefano Montoli for the analysis carried on with MDBN. ...

doi:10.1093/jamiaopen/ooy008 pmid:31984320 pmcid:PMC6951984 fatcat:bi2zym55abe65bqlwo2fl5wwna

DOAJ

The method may be used for an effective screening of potentially interesting genotype-phenotype and biomarker-phenotype associations in genome-wide studies, which may have important implications for validating ... The framework builds on sparse linear methods developed for regression and modified here for inferring causal structures of richer networks with latent variables. ... As a special case, it also includes sparse conditional factor analysis. ...

dblp:conf/nips/AgakovMKS10 fatcat:epkj3zpa7zhkliofwxa67ppl4u

The emergence of the recommendation system has effectively alleviated the information overload problem. ... ., facing the sparsity problem, or adopt the fully connected network to concatenate the attribute information, ignoring the interaction between the attribute information. ... [23] proposed a novel probability framework, named as joint matrix factorization (JMF), which can effectively extract side information to form latent vectors. ...

doi:10.3390/a14100281 fatcat:sujap4c2qzb57pc75jl3fanpvm

DOAJ Szczepanski

The vast majority of completed FEA studies are based on non-occluded faces collected in a controlled laboratory environment. ... Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment and ... Sparse Representation Approach The sparse representation approach was firstly proposed for face recognition tasks in [Wright et al. 2009 ], and later applied into FEA, especially from occluded faces ...

doi:10.1145/3158369 fatcat:xmfmw7z275hb7e6dbazjqm5fui

Multiple Versions

Ideally, we would like these representations to disentangle the unknown factors of variation that underlie the training distribution. ... This paper focuses on the context of the Unsupervised and Transfer Learning Challenge, on why unsupervised pre-training of representations can be useful, and how it can be exploited in the transfer learning ... to make the hidden representation sparse. ...

dblp:journals/jmlr/Bengio12 fatcat:2ehsmuuqsrggpkbb3s37yfflnm

Szczepanski

Face Aging Effect Simulation Using Hidden Factor Analysis Joint Sparse Representation

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Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches [article]

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Age of acquisition effects in adult lexical processing reflect loss of plasticity in maturing systems: Insights from connectionist networks

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Age of acquisition effects in adult lexical processing reflect loss of plasticity in maturing systems: Insights from connectionist networks

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A Survey of Face Recognition Methods based on Deep Learning

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Face Age Estimation and the Other-race Effect

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A Fault Diagnosis Methodology based on Non-stationary Monitoring Signals by Extracting Features with Unknown Probability Distribution

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A Novel Face Recognition Algorithm based on the Deep Convolution Neural Network and Key Points Detection Jointed Local Binary Pattern Methodology

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Hybrid Recommender System based on Autoencoders

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Hybrid Collaborative Filtering with Autoencoders [article]

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Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia

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Sparse Instrumental Variables (SPIV) for Genome-Wide Studies

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Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation

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Facial Expression Analysis under Partial Occlusion

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Deep Learning of Representations for Unsupervised and Transfer Learning

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