Modeling of facial aging and kinship: A survey

Georgopoulos, Markos; Panagakis, Yannis; Pantić, Maja

doi:10.1016/j.imavis.2018.05.003

Cited by 26 publications

(19 citation statements)

References 220 publications

(296 reference statements)

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“…Synthesizing faces of a specific target demographic has been studied extensively, albeit for individual demographic attributes. In particular, age progression refers to the task of rendering an aged or rejuvenated image of an input face (Fu et al 2010;Ramanathan et al 2009;Georgopoulos et al 2018). While earlier works in age progression proposed simplistic prototype-based approaches that could not produce photorealistic results, a number of recently proposed GAN-based methods are capable of convincing face aging.…”

Section: Transfer Of Demographic Attributesmentioning

confidence: 99%

“…Deep learning-based models have been successfully utilized to advance the state-of-the-art in face analysis, resulting in accurate algorithms for the recognition of the identity Masi et al (2018), age (Fu et al 2010;Georgopoulos et al 2018), gender, (Ng et al 2012) and expressions (Li and Deng 2020) of the human face. Building on this success, automatic face analysis facilitates modern human-computer interaction and has found application in numerous areas of everyday life.…”

Section: Introductionmentioning

confidence: 99%

“…Translation of demographic attributes has been studied extensively, albeit for individual attributes. In particular, facial aging is a long-standing task in computer vision (Ramanathan et al 2009;Fu et al 2010;Georgopoulos et al 2018) with recent GAN-based approaches being able to produce realistically aged and rejuvenated faces Wang et al 2018;Duong et al 2019). On the other hand, modifying the gender of a face is a common application of facial attribute transfer (Choi et al 2018;He et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Mitigating Demographic Bias in Facial Datasets with Style-Based Multi-attribute Transfer

et al. 2021

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Deep learning has catalysed progress in tasks such as face recognition and analysis, leading to a quick integration of technological solutions in multiple layers of our society. While such systems have proven to be accurate by standard evaluation metrics and benchmarks, a surge of work has recently exposed the demographic bias that such algorithms exhibit–highlighting that accuracy does not entail fairness. Clearly, deploying biased systems under real-world settings can have grave consequences for affected populations. Indeed, learning methods are prone to inheriting, or even amplifying the bias present in a training set, manifested by uneven representation across demographic groups. In facial datasets, this particularly relates to attributes such as skin tone, gender, and age. In this work, we address the problem of mitigating bias in facial datasets by data augmentation. We propose a multi-attribute framework that can successfully transfer complex, multi-scale facial patterns even if these belong to underrepresented groups in the training set. This is achieved by relaxing the rigid dependence on a single attribute label, and further introducing a tensor-based mixing structure that captures multiplicative interactions between attributes in a multilinear fashion. We evaluate our method with an extensive set of qualitative and quantitative experiments on several datasets, with rigorous comparisons to state-of-the-art methods. We find that the proposed framework can successfully mitigate dataset bias, as evinced by extensive evaluations on established diversity metrics, while significantly improving fairness metrics such as equality of opportunity.

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Section: Transfer Of Demographic Attributesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mitigating Demographic Bias in Facial Datasets with Style-Based Multi-attribute Transfer

et al. 2021

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“…Up to now, a significant volume of research has been done in facial age estimation [8] which can be divided into two main categories: traditional machine learning approaches and deep learning methods.…”

Section: Related Workmentioning

confidence: 99%

Multi‐stage age estimation using two level fusions of handcrafted and learned features on facial images

Taheri

Toygar

2018

IET biom.

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Age estimation from facial images is an important application of biometrics. In contrast to other facial variations like occlusions, illumination, misalignment and facial expressions, ageing variation is affected by human genes, environment, lifestyle and health which make age estimation a challenging task. In this study, the authors propose a new age estimation system which exploits multi-stage features from a generic feature extractor, a trained convolutional neural network (CNN), and precisely combined these features with a selection of age-related handcrafted features. This method utilises a decision-level fusion of estimated ages by two different approaches; the first one uses feature-level fusion of different handcrafted local feature descriptors for wrinkle, skin and facial component, while the second one uses score-level fusion of different feature layers of a CNN for its age estimation. Experiments on the publicly available MORPH-Album-2 and FG-NET databases prove the effectiveness of the novel method. Moreover, an additional experimental study on AgeDB database demonstrates that the proposed method is comparable with the best state-of-the-art system for age estimation using in-the-wild age databases.

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“…The problem of estimating the age of human via a face image is deemed as a very active research issue in the last decades, due to the need to provide and develop the property or ability to determine the age of individuals from their faces (see Fig. 1) in many real-world applications, for example forensics, crime detection and prevention, identification of persons missing for several years, surveillance systems for suspects discovery, facial recognition and verification, access control for web content based on age, age simulation, cosmetics surgery, biometrics, and many other fields [2,3].…”

Section: Introductionmentioning

confidence: 99%

Semantic Human Face Analysis for Multi-level Age Estimation

Howyan¹,

Jaha²

2022

Intelligent Automation &Amp; Soft Computing

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Human face is one of the most widely used biometrics based on computer-vision to derive various useful information such as gender, ethnicity, age, and even identity. Facial age estimation has received great attention during the last decades because of its influence in many applications, like face recognition and verification, which may be affected by aging changes and signs which appear on human face along with age progression. Thus, it becomes a prominent challenge for many researchers. One of the most influential factors on age estimation is the type of features used in the model training process. Computer-vision is characterized by its superior ability to extract traditional facial features such as shape, size, texture, and deep features. However, it is still difficult for computers to extract and deal with semantic features inferred by human-vision. Therefore, we need somehow to bridge the semantic gap between machines and humans to enable utilization of the human brain capabilities of perceiving and processing visual information in semantic space. Our research aims to exploit human-vision in semantic facial feature extraction and fusion with traditional computer-vision features to obtain integrated and more informative features as an initial study paving the way to further augment the outperforming state-of-the-art age estimation models. A hierarchical automatic age estimation is achieved upon two consecutive stages: classification to predict (high-level) age group, followed by regression to estimate (low-level) exact age. The results showed noticeable performance improvements, when fusing semantic-based features with traditional vision-based features, surpassing the performance of traditional features alone.

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Modeling of facial aging and kinship: A survey

Cited by 26 publications

References 220 publications

Mitigating Demographic Bias in Facial Datasets with Style-Based Multi-attribute Transfer

Mitigating Demographic Bias in Facial Datasets with Style-Based Multi-attribute Transfer

Multi‐stage age estimation using two level fusions of handcrafted and learned features on facial images

Semantic Human Face Analysis for Multi-level Age Estimation

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