A hierarchical approach to facial aging

Sethuram, Amrutha; Ricanek, Karl; Patterson, Eric

doi:10.1109/cvprw.2010.5543611

Cited by 12 publications

(6 citation statements)

References 18 publications

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“…Each group includes an input face, a ground truth and three aging results generated by different methods. The number or word under each face photo represents the age range (e.g., [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80] or the age period (e.g., older). For convenience of comparison, black background has been added to each face photo.…”

Section: Experimental Settingsmentioning

confidence: 99%

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Personalized Age Progression with Bi-Level Aging Dictionary Learning

Shu

Tang

et al. 2018

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Age progression is defined as aesthetically re-rendering the aging face at any future age for an individual face. In this work, we aim to automatically render aging faces in a personalized way. Basically, for each age group, we learn an aging dictionary to reveal its aging characteristics (e.g., wrinkles), where the dictionary bases corresponding to the same index yet from two neighboring aging dictionaries form a particular aging pattern cross these two age groups, and a linear combination of all these patterns expresses a particular personalized aging process. Moreover, two factors are taken into consideration in the dictionary learning process. First, beyond the aging dictionaries, each person may have extra personalized facial characteristics, e.g. mole, which are invariant in the aging process. Second, it is challenging or even impossible to collect faces of all age groups for a particular person, yet much easier and more practical to get face pairs from neighboring age groups. To this end, we propose a novel Bi-level Dictionary Learning based Personalized Age Progression (BDL-PAP) method. Here, bi-level dictionary learning is formulated to learn the aging dictionaries based on face pairs from neighboring age groups. Extensive experiments well demonstrate the advantages of the proposed BDL-PAP over other state-of-the-arts in term of personalized age progression, as well as the performance gain for cross-age face verification by synthesizing aging faces.

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Section: Experimental Settingsmentioning

confidence: 99%

“…Some prior works related to the age progression have posted their best face aging results with input faces at different ages, including [3], [59], [1], [37], [60], [61], [9], [62], [63], [36], [30] and [5]. There are 261 5. http://cherry.dcs.aber.ac.uk/Transformer/ aging results with 87 input faces in total.…”

Section: Quantitative Comparison With Prior Workmentioning

confidence: 99%

Personalized Age Progression with Bi-Level Aging Dictionary Learning

Shu

Tang

et al. 2018

IEEE Trans. Pattern Anal. Mach. Intell.

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“…Baseline methods: Some prior works on age progression have posted their best face aging results with inputs of di erent ages, including [36,39]. We mainly compare with 9 baselines, including FT demo: an online fun demo Face Transformer, IAAP: state-of-theart illumination-aware age progression [15], RFA: recurrent face aging [40], CDL: coupled dictionary learning [35], acGAN: face aging with conditional generative adversarial networks [2], CAAE: conditional adversarial autoencoder [46], CDM: Compositional and Dynamic Model [36] and [33,39]. ere are 246 aging results with 72 inputs in total.…”

Section: Comparison With the State-of-the-artsmentioning

confidence: 99%

Face Aging with Contextual Generative Adversarial Nets

Liu

Sun

Zhu

et al. 2017

Proceedings of the 25th ACM International Conference on Multimedia

114

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Face aging, which renders aging faces for an input face, has a racted extensive a ention in the multimedia research. Recently, several conditional Generative Adversarial Nets (GANs) based methods have achieved great success. ey can generate images ing the real face distributions conditioned on each individual age group. However, these methods fail to capture the transition pa erns, e.g., the gradual shape and texture changes between adjacent age groups. In this paper, we propose a novel Contextual Generative Adversarial Nets (C-GANs) to speci cally take it into consideration. e C-GANs consists of a conditional transformation network and two discriminative networks. e conditional transformation network imitates the aging procedure with several specially designed residual blocks. e age discriminative network guides the synthesized face to t the real conditional distribution. e transition pa ern discriminative network is novel, aiming to distinguish the real transition pa erns with the fake ones. It serves as an extra regularization term for the conditional transformation network, ensuring the generated image pairs to t the corresponding real transition pa ern distribution. Experimental results demonstrate the proposed framework produces appealing results by comparing with the state-of-the-art and ground truth. We also observe performance gain for cross-age face veri cation.

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“…A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples [13], [14]. 2277-6451, pp.…”

Section: Support Vector Machinementioning

confidence: 99%

Automatic Age Estimation Based on LBP and GLCM Features Using SVM

Rampay

2017

IJARCSSE

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Face is generally considered as the reference frame of mind. Therefore, to estimate the feeling of the mind, many authors have considered the emotions from the facial expressions into consideration to identify the state of mind of an individual. Hence in this article we proposed a methodology for automatic age estimation based on Local Binary Pattern (LBP) and Grey Level Co- Occurrence Matrix (GLCM). The facial features are extracted using LBP and GLCM and these features are given as input’s to the Support Vector Machine (SVM) for age estimation. The experimentation on proposed method is carried out using FG-NET database and Mean Absolute Error (MAE) is calculated to compare the proposed method with state-of-the-art algorithms. Finally, the proposed methodology demonstrates the classification accuracy above 88%.

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A hierarchical approach to facial aging

Cited by 12 publications

References 18 publications

Personalized Age Progression with Bi-Level Aging Dictionary Learning

Personalized Age Progression with Bi-Level Aging Dictionary Learning

Face Aging with Contextual Generative Adversarial Nets

Automatic Age Estimation Based on LBP and GLCM Features Using SVM

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