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This paper tackled the problem of human facial age estimation using transfer learning of some pre-trained CNNs, namely VGG, Res-Net, Google-Net, and Alex-Net.
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In the environment of smart cities, human facial age estimation has become an important research topic due to its wide applications.
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Facial Age sentence examples within Accurate Facial Age
Accurate facial age estimation is quite challenging, since ageing process is dependent on gender, ethnicity, lifestyle and many other factors, therefore actual age and apparent age can be quite different.
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These findings show that facial aging is an asymmetric process which plays role in accurate facial age estimation.
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Facial Age sentence examples within facial age estimation
This paper tackled the problem of human facial age estimation using transfer learning of some pre-trained CNNs, namely VGG, Res-Net, Google-Net, and Alex-Net.
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Facial Age sentence examples within facial age assessment
This paper tackled the problem of human facial age estimation using transfer learning of some pre-trained CNNs, namely VGG, Res-Net, Google-Net, and Alex-Net.
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Changes may also follow normal dentofacial ageing and are unpredictable with great variability.
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Automated facial age estimation has drawn increasing attention in recent years.
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Therefore, in this paper, a facial age progression model that captures non-linear age variances is designed by using a deep learning-based method called Generative Adversarial Network.
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Experimental results on three benchmark databases demonstrated the effectiveness and efficiency of the proposed method on facial age estimation in comparison to previous state-of-the-art methods.
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Face aging (FA) for young faces refers to rendering the aging faces at target age for an individual, generally under 20s, which is an important topic of facial age analysis.
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Oligomers comprising four or sixteen phenylalanine residues with regularly intercalated aliphatic chains of different lengths prepared by solid-phase synthesis exhibit sufficient thermal stability to be used as interfacial agents and processed for the preparation of poly(propylene-co-ethylene)-based composite materials.
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Background There is evidence that changes to the midface and lower third of the face in isolation contribute significantly to one’s perception of the overall facial age.
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The periocular area is one of the first areas to show the signs of facial ageing.
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In a multi‐ethnic and multi‐centre study, we previously documented similar patterns of female facial age assessments across ethnicities, influenced by gender and ethnicity of assessors.
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In this study, polydopamine (PDA) was used as an interfacial agent to enhance the combination between carbon nanotubes (CNTs) and cotton fiber to fabricated a highly conductive fabric.
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The proposed generative adversarial network algorithm is a unified framework that combines facial age estimation and age-separated face verification.
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This suggests that the facial morphological features contained in facial landmarks can reflect facial age better than facial texture features.
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Using AgeVox-Celeb, our paper makes the following contributions: (i) A facial age estimation model can outperform a speech age estimation model by comparing the state-of-the-art models in each task.
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Altogether, we show that, among other schemes, our method exemplifies facial age-group estimation.
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However, the previous findings regarding on whether facial age influences interpersonal trust are inconsistent.
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The techniques of facial age prediction and classification are commonly used in the recent years for vitality applications but these techniques are time-consuming.
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However, in applications like vision-based facial age estimation, providing the exact labels (age of a person) may be challenging even for human annotators, as it maybe difficult to accurately estimate the age of a person merely from a facial image; it maybe much easier to provide relative label feedback, such as whether a particular subject is older than another subject.
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Furthermore, we demonstrate the power of our novel curriculum learning method, which improved the classification accuracy of our facial age estimator from 46% to 62% and its F1 score from 0.
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, facial age estimation and image esthetic assessment, showing significant improvements and better stability over the state-of-the-art OR methods.
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In this article, we propose a knowledge distillation approach with two teachers for facial age estimation.
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Facial ageing occurs as a consequence of multifactorial changes in both the external skin and underlying tissues.
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Facial age assessments correlated negatively with attractiveness and health assessments.
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Facial age estimation is an essential feature in many applications satisfying the need to provide users with content that corresponds to their ages.
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It is with great interest that we read the authors’ manuscript on aesthetic facial rejuvenation after orthognathic surgery [1], having long recognized the influence of skeletal support on both the development and correction of facial ageing [2].
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With facial ageing being a progressive, 3-dimensional process occurring in multiple layers, that would mean an ideal filler product could be used for many different indications: bone restoration, soft tissue volume restoration, collagen/elastin regeneration, dermal thickening and tightening and so on.
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We also introduce two new datasets: (i) Pornographic-2M, which contains two million pornographic images, and (ii) Juvenile-80k, including 80k manually labeled images with apparent facial age.
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Facial ageing involves several key processes: loss of volume; laxity of tissues; and surface skin changes of wrinkles, pigmentation, vascular lesions, and textural changes.
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Our comprehensive experiments show that improving a typical Deep Convolutional Neural Network (CNN) architecture with facial age augmentation improves both the accuracy and standard deviation of the classifier when predicting emotions of diverse age groups including seniors.
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Unlike the conventional facial age estimation approaches which utilize fully-visible faces as input data that does not generalize well for occlusion images, our approach aims to ignore the occlusion and only focus on the non-occluded facial areas so that we can improve the occluded facial age estimation accuracy.
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The most existing studies in the facial age estimation assume training and test images are captured under similar shooting conditions.
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In the environment of smart cities, human facial age estimation has become an important research topic due to its wide applications.
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Experimental results show that compared to the DLDL, our method is more effective for facial age recognition.
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In recent times, human facial age estimation topic attracted a lot of attention due to its ability to improve biometrics systems.
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The progression/regression of facial age can be applied to cross-age recognition or entertainment-related applications.
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We propose the Wasserstein Divergence GAN with an identity expert and an attribute retainer for facial age transformation.
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The experimental results on three applications with semantically correlated classes, including facial age estimation, head pose estimation, and image esthetic assessment, validate the theoretical insights gained by our analysis and demonstrate the usefulness of the proposed loss functions in practical applications.
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However, there is currently no consensus regarding how the brain is processing facial cues related to age, and if facial age processing changes as a function of the age of the observer (i.
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Upon reviewing the literature on facial age estimation, we notice that few articles tackle this low quality image based facial age estimation problem.
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Herein, we investigated the role of fluorinated ionic liquid (IL) as a new interfacial agent in poly(vinylidene fluoride-co-chlorotrifluoroethylene) (P(VDF-CTFE))/graphene composite films.
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The same impregnation conditions provided increments in tensile toughness of 52 and 31%, respectively, showing the positive role of the nanostructure as a strong interfacial agent.
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We report on the preparation and efficacy of 10‐hydroxystearic acid (HSA) that improves facial age spots and conspicuous pores.
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We constructed a robust age predictor and found that on average people of the same chronological age differ by +/-6 years in facial age, with the deviations increasing after age 40.
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The huge data resource on the Web provides us with an emerging chance to solve the lack of training sample problem that lasting for years in facial age estimation.
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Two-stage Cumulative Attribute (CA) regression has been found effective in regression problems of computer vision such as facial age and crowd density estimation.
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Accurate facial age estimation is quite challenging, since ageing process is dependent on gender, ethnicity, lifestyle and many other factors, therefore actual age and apparent age can be quite different.
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Upon reviewing the literature on facial age estimation, we notice that few articles tackle this low quality image based facial age estimation problem.
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sad) across facial ages (infant vs.
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BACKGROUND: Aging is an increasing concern of modern society, particularly facial ageing.
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However, comparatively little is known about the effect of frontal recession on the perceived facial age (PFA) of East Asian males.
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Many studies regarding facial age estimation mainly focus on two aspects: facial aging feature extraction and classification/regression model learning.
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Facial age estimation has increasingly gained attention in the Computer Vision and Image Processing research community due to its numerous applications in several domains.
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Hence, in this paper, we have surveyed and compared all the neural network models developed and implemented for facial age estimation from 2010 to 2019.
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Human facial age estimation has extensive range of real-world application in security control, law enforcement, and human computer interaction.
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In this paper, we propose an ordinal deep learning approach for facial age estimation.
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Facial age estimation from images is a challenging task, especially if the subjects are older, since idiosyncratic variations increase with age, and lifestyle factors have an impact on the appearance.
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Facial age estimation based on discrete-wavelet-transform deep convolutional neural networks (DWT-DCNN) is a biometric application based on deep learning.
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The addition of interfacial agent can significantly improve the adhesion between emulsified asphalt and aggregates.
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These findings show that facial aging is an asymmetric process which plays role in accurate facial age estimation.
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The superficial musculoaponeurotic system (SMAS) is an anatomical structure involved in the facial ageing process.
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Facial age estimation techniques are extensively used in vitality applications nowadays; however, they are a time-consuming task.
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Here, we develop a universal SERS-based approach toward quantification of A/G in single-stranded DNAs (12 up to 28 bases) by introducing a novel interfacial agent, dichloromethane.
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On the other hand, in the case of compatibilized PP/ECH blends, MA-g-PP acted as a strong interfacial agent between PP and ECH phases and led to outstanding improvement in the mechanical and rheological properties.
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The main challenge in automatic facial age estimation task comes from the large intra-class facial appearance variations due to both gender and race attributes.
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This research attempts to evaluate how humans perceive facial age and their ability to recognize age-separated faces.
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