Face aging simulation and recognition based on NMF algorithm with sparseness constraints

Du, Ji-Xiang; Zhai, Chuan-Min; Ye, Yong-Qing

doi:10.1016/j.neucom.2012.08.030

Cited by 26 publications

(7 citation statements)

References 22 publications

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“…Ji-Xiang Du et al [4], as of late, face recognition has been generally connected in overseeing and criminal fields. Aside from lighting, signal and appearance, varieties fit as a fiddle and surface of human faces because of aging component would likewise influence the execution of face recognition frameworks to a great degree.…”

Section: Fig 4 Face Time Lapsementioning

confidence: 99%

A Survey on Face Recognition based on Age Invarient Technique

Bhattacharya¹

2019

IJRASET

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Human face check is an essential point in PC vision, imaging and multimedia as it characterizes the uniqueness and passes on one's personality. Recognition of face or personality is required to get to the rendered benefits by the licit individual as it were. Applications incorporate access to PC framework, ATMs, workstations, cell phones and so forth. Verification exactness may get fizzled in the event that it experiences any of the components recorded as variety in face pose, outward appearance, illumination and maturing. This study paper presents the panoramic perspective of movement in face recognition appropriate from the customary strategies to the most developed advances. Applications in various field, for example, security, machine recognition of faces in non-law authorizations (like travel permits, picture IDs, driving licenses) has been said. It additionally features the system (parameters, methods, database, machine learning strategies and so forth).

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Section: Fig 4 Face Time Lapsementioning

confidence: 99%

A Survey on Face Recognition based on Age Invarient Technique

Bhattacharya¹

2019

IJRASET

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“…Several methods of NMF are discussed here, which include: Semi supervised constrained NMF [19], semisupervised graph based discriminative NMF [20], Bayesian learning approach to reduce the generalization error in upper bound using NMF [21] and update rules [22], sparseness NMF, which provides better characterization of the features [23], sparse unmixing NMF [24], locally weighted sparse graph regularized NMF [25], graph-regularized NMF [26], graph dual regularization [27], multiple graph regularized NMF [28], graph regularized multilayer NMF [29], adaptive graph regularized NMF [30], hyper-graph regularized [31], graph regularization with sparse NMF [32], multi-view NMF [33], extended incremental NMF [34], incremental orthogonal projective NMF [35], correntropy induced metric NMF [36], multi-view NMF [37], patch based NMF [38], MMNMF [39], regularized NMF [40], FR conjugate gradient NMF [41]. However, these methods failed to address the problems associated with non-orthogonality due to the presence of nonnegative elements in NMF.…”

Section: Related Workmentioning

confidence: 99%

Reducing Dimensionality in Text Mining using Conjugate Gradients and Hybrid Cholesky Decomposition

Alostad¹

2017

ijacsa

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Abstract-Generally, data mining in larger datasets consists of certain limitations in identifying the relevant datasets for the given queries. The limitations include: lack of interaction in the required objective space, inability to handle the data sets or discrete variables in datasets, especially in the presence of missing variables and inability to classify the records as per the given query, and finally poor generation of explicit knowledge for a query increases the dimensionality of the data. Hence, this paper aims at resolving the problems with increasing data dimensionality in datasets using modified non-integer matrix factorization (NMF). Further, the increased dimensionality arising due to non-orthogonally of NMF is resolved with Cholesky decomposition (cdNMF). Initially, the structuring of datasets is carried out to form a well-defined geometric structure. Further, the complex conjugate values are extracted and conjugate gradient algorithm is applied to reduce the sparse matrix from the data vector. The cdNMF is used to extract the feature vector from the dataset and the data vector is linearly mapped from upper triangular matrix obtained from the Cholesky decomposition. The experiment is validated against accuracy and normalized mutual information (NMI) metrics over three text databases of varied patterns. Further, the results prove that the proposed technique fits well with larger instances in finding the documents as per the query, than NMF, neighborhood preserving: nonnegative matrix factorization (NPNMF), multiple manifolds non-negative matrix factorization (MMNMF), robust non-negative matrix factorization (RNMF), graph regularized non-negative matrix factorization (GNMF), hierarchical non-negative matrix factorization (HNMF) and cdNMF.

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“…The earliest attempts on cross-age face recognition fell into the category of generative approaches [5,6], which rely on simulating the face aging process and augmenting the datasets with synthetic samples to improve the performance. However, these methods suffer from the computationally expensive problem due to the complexity of the simulating model itself, and it lacks diversity among the generated samples due to a strong parametric assumption of the model.…”

Section: Introductionmentioning

confidence: 99%

MAM: Multiple Attention Mechanism Neural Networks for Cross-Age Face Recognition

Ren

Wang

2022

Wireless Communications and Mobile Computing

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Cross-age face recognition problem is of great challenge in practical applications because face features of the same person at different ages contain variant aging features in addition to the invariant identity features. To better extract the age-invariant identity features hiding beneath the age-variant aging features, a deep learning-based approach with multiple attention mechanisms is proposed in this paper. First, we propose the stepped local pooling strategy to improve the SE module. Then by incorporating the residual-attention mechanism, the self-attention mechanism, and the improved channel-attention mechanism to the backbone network, we proposed the Multiple Attention Mechanism Network (MAM-CNN) framework for the cross-age face recognition problem. The proposed framework can focus on essential face regions to highlight identity features and diminish the distractions caused by aging features. Experiments are carried out on two well-known public domain face aging datasets (MORPH and CACD-VS). The results yielded prove that the introduced multiple mechanisms jointly enhance the model performance by 0.96% and 0.52%, respectively, over the state-of-the-art algorithms.

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Face aging simulation and recognition based on NMF algorithm with sparseness constraints

Cited by 26 publications

References 22 publications

A Survey on Face Recognition based on Age Invarient Technique

A Survey on Face Recognition based on Age Invarient Technique

Reducing Dimensionality in Text Mining using Conjugate Gradients and Hybrid Cholesky Decomposition

MAM: Multiple Attention Mechanism Neural Networks for Cross-Age Face Recognition

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