Face Aging Simulation Based on NMF Algorithm with Sparseness Constraints

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

doi:10.1007/978-3-642-25944-9_67

Cited by 15 publications

(8 citation statements)

References 5 publications

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“…An ensemble SNMF process was proposed in [23] to represent data instances in parts and partition the data space into localities, and then the individual classifiers in each locality were coordinated for final classification in videos concept detection. A face aging simulation method based on sparse-constrained method was proposed in [24] and then applied in the age-across face recognition.…”

Section: Related Workmentioning

confidence: 99%

Sparse General Non-Negative Matrix Factorization Based on Left Semi-Tensor Product

Chen

Peng

et al. 2019

IEEE Access

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The dimension reduction of large scale high-dimensional data is a challenging task, especially the dimension reduction of face data and the accuracy increment of face recognition in the large scale face recognition system, which may cause large storage space and long recognition time. In order to further reduce the recognition time and the storage space in the large scale face recognition systems, on the basis of the general non-negative matrix factorization based on left semi-tensor (GNMFL) without dimension matching constraints proposed in our previous work, we propose a sparse GNMFL/L (SGNMFL/L) to decompose a large number of face data sets in the large scale face recognition systems, which makes the decomposed base matrix sparser and suppresses the decomposed coefficient matrix. Therefore, the dimension of the basis matrix and the coefficient matrix can be further reduced. Two sets of experiments are conducted to show the effectiveness of the proposed SGNMFL/L on two databases. The experiments are mainly designed to verify the effects of two hyper-parameters on the sparseness of basis matrix factorized by SGNMFL/L, compare the performance of the conventional NMF, sparse NMF (SNMF), GNMFL, and the proposed SGNMFL/L in terms of storage space and time efficiency, and compare their face recognition accuracies with different noises. Both the theoretical derivation and the experimental results show that the proposed SGNMF/L can effectively save the storage space and reduce the computation time while achieving high recognition accuracy and has strong robustness.INDEX TERMS Machine learning, unsupervised learning, semi-tensor product (STP), sparse general non-negative matrix factorization (SGNMF).

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Section: Related Workmentioning

confidence: 99%

Sparse General Non-Negative Matrix Factorization Based on Left Semi-Tensor Product

Chen

Peng

et al. 2019

IEEE Access

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“…We compare our HFA model against several state-of-theart methods for age invariant face recognition on MORPH Album 2. They include (i) FaceVACS, a leading commercial face recognition engine [5], (ii) several newly developed generative methods [7,27] for face aging, and (iii) several newly developed discriminative methods [14,21,26] for direct age invariant face recognition. The comparative results are reported in Table 4.…”

Section: Experiments On the Morph Ablum 2 Datasetmentioning

confidence: 99%

“…The research on age related face image analysis has only been studied in recent years. Most existing works focus on age estimation [8, 10-12, 17, 18, 24, 28, 37, 40, 41] and aging simulation [7,19,27,30,31,35]. However, work that explicitly tackles age invariant face recognition is limited.…”

Section: Introductionmentioning

confidence: 99%

“…Existing methods on age invariant face recognition roughly fall into two categories: generative approaches and the discriminative approaches. Generative methods try to synthesis face images that match the target age before recognition [7,10,19,27]. They try to construct a 2-D or 3-D generative model to compensate for the aging process in face matching.…”

Section: Introductionmentioning

confidence: 99%

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Hidden Factor Analysis for Age Invariant Face Recognition

Gong

Lin

et al. 2013

2013 IEEE International Conference on Computer Vision

160

133

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Age invariant face recognition has received increasing attention due to its great potential in real world applications. In spite of the great progress in face recognition techniques, reliably recognizing faces across ages remains a difficult task. The facial appearance of a person changes substantially over time, resulting in significant intra-class variations. Hence, the key to tackle this problem is to separate the variation caused by aging from the person-specific features that are stable. Specifically, we propose a new method, called Hidden Factor Analysis (HFA). This method captures the intuition above through a probabilistic model with two latent factors: an identity factor that is age-invariant and an age factor affected by the aging process. Then, the observed appearance can be modeled as a combination of the components generated based on these factors. We also develop a learning algorithm that jointly estimates the latent factors and the model parameters using an EM procedure. Extensive experiments on two well-known public domain face aging datasets: MORPH (the largest public face aging database) and FGNET, clearly show that the proposed method achieves notable improvement over state-of-the-art algorithms.

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“…The contribution of this work is an integrated system, which given a person's face that belongs to a certain age group, it predicts its shape and texture in a target age group. Despite the fact that in the past age progression techniques that combine shape and texture manipulations were reported (Du et al, 2012), (Lanitis et al, 2002), (Park et al, 2008) in our method, shape and texture information is treated separately through the use of age specific 3D shape and texture models. Using 3D instead of 2D models enables the application of the proposed method on faces with different orientations.…”

Section: Introductionmentioning

confidence: 99%

Facial Age Simulation using Age-specific 3D Models and Recursive PCA

Maronidis¹,

Lanitis²

2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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Facial age simulation is a topic that has been gaining increasing interest in computer vision. In this paper, a novel age simulation method that utilizes age-specific shape and texture models is proposed. During the process of generating age-specific shape models, 3D face measurements acquired from real human faces are used in order to tune a generic 3D face shape model to represent face shapes belonging to certain age groups. A number of diagnostic studies have been conducted in order to validate the compatibility of the tuned shape models with the corresponding age groups. The shape age-simulation process utilizes agespecific shape models that incorporate age-related constraints during a 3D shape reconstruction phase. Age simulation is completed by predicting the texture at the target age based on a recursive PCA method that aims to superimpose age-related texture modifications in a way that preserves identity-related characteristics of the subject in the source image. Preliminary results indicate the potential of the proposed method.

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Face Aging Simulation Based on NMF Algorithm with Sparseness Constraints

Cited by 15 publications

References 5 publications

Sparse General Non-Negative Matrix Factorization Based on Left Semi-Tensor Product

Sparse General Non-Negative Matrix Factorization Based on Left Semi-Tensor Product

Hidden Factor Analysis for Age Invariant Face Recognition

Facial Age Simulation using Age-specific 3D Models and Recursive PCA

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