Due to the broad application prospects in cross-age face recognition and various entertainment projects, face aging has attracted extensive attention from computer vision, graphics, psychology and ...other fields. In recent years, the method based on Generative Adversarial Networks(GANs) has achieved great success in generating high quality images. Among them, as a special case of GANs, Conditional GANs (CGANs) introduces prior information in the process of image generation to guide the generator to generate sample images with specific conditions. Inspired by CGANs, and in order to solve some problems in face aging research: (1) the accuracy of aging; (2) the aging effect is realistic and natural; (3) identity information is invariant, we propose a face aging simulation method based on conditional cycle loss and the principle of homology continuity. Different from the previous CGANs method for aging process simulation, we reconstruct the input face by using the synthesized aging face and the age label of the input face. By minimizing the reconstruction loss, the identity information during the process of face aging is maintained. At the same time, in order to ensure the accuracy of aging, we introduce an age classification network based on the Principle of Homology-Continuity, which is more consistent with the process of human "cognition". The experimental results show that the proposed method generates pleasing face aging results, and significantly reduces the number of parameter.
Given a facial image of a particular person, aging is to construct the facial image of the same person but at a different age. This assumption becomes an important limitation when modelling human ...aging from real data, especially in order to be used by plastic surgeons. We present a new approach to robustly analyze parts of face aging. Our analysis-synthesis cooperation, possible thanks to a highly realistic 3D head model and in comparison with a youthful age face of the same person. A prototype of aging a specific region of someone's face, which utilizes a framework for facial feature is presented. Via a number of experiments the validity of the rules that have been employed are demonstrated. The evaluation of the overall performance of the fully automated system indicates that facial aging is performed quite accurately by the system.
Face Aging has been an vital area of research for the past few decades. As the age increases, there are some visible changes in the face, making age classification simpler. Based on the facial ...growth, we can classify the human age into various kinds. Though there are various algorithms existed so far, a more sophisticated method is attempted for classifying facial age. Age Prototypes, Statistical models and Distance based technique have been widely used for classification of human face. The system can be improved by using the Wavelet Transformation (WT) for extracting the face features and Artificial Neural Network to classify the age group. The facial images are pre-processed and then the face features are extracted using Wavelet Transformation. The distance between each of features are evaluated using Euclidean distance and these values were given as input to Adaptive Resonance Network (ART). The Neural Network is trained using FG-NET (Face and Gesture Recognition Research Network) aging database. The human age is classified into four categories as Child (0-12 years), Adolescence (13-18 years), Adult (19-59 years) and Senior Adult (60 years and above) which is discussed in the paper.
This paper introduces an advanced age-determination technique using hybrid facial features and Kernel Spectral Regression, a nonlinear dimensionality reduction method. In the preprocessing stage, the ...logarithmic nonsubsampled contourlet transform (NSCT) is conducted to denoise and amplify facial wrinkles that help to distinguish young faces from elder ones. Then the hybrid facial features that combine both local and holistic features are extracted from the preprocessed images. Our novel Uniform Local Ternary Patterns (ULTP) are used as the local features. Meanwhile the holistic features are extracted by using the Active Appearance Model (AAM) to encode each face. Kernel Spectral Regression is used to minimize inter-class distances while maximizing intra-class distances of feature sets. These reduced features are used to classify faces into two age groups (age-classification). An age-determination function is then constructed for each age group in accordance with physiological growth periods for humans - pre-adult (youth) and adult. Compared to published results, this method yields promising results in overall mean absolute error (MAE), mean absolute error per decade of life (MAE/D), and cumulative match score in various face aging corpuses.
This paper presents an advanced age-determination technique that combines holistic and local features derived from an image of the face. A 30×1 Active Appearance Model (AAM) linear encoding of each ...face is produced to work as holistic features. Meanwhile, local features are extracted by using Local Ternary Patterns (LTP). These combined features are used to classify faces into one of two age groups (age-classification). An age-determination function is then constructed for each age group in accordance with physiological growth periods for humans - pre-adult (youth) and adult. Compared to published results, this method yields the highest accuracy rates in overall mean absolute error (MAE), mean absolute error per decade of life (MAE/D), and cumulative match score.
Facing aging is an attracting and challenging work in the area of computer vision and it has been widely used in many fields. In this paper, a novel method of simulating this complex aging process is ...proposed. We get the aging result through determining of feature points, triangulating and piece-wise affine warping. And importantly, we also improve the approach in two ways, making the result more realistic and natural. Our work and experiment demonstrate that the method used in face aging simulation is applicable.
Classification of face images can be done in various ways. This research uses two-dimensional photographs of people's faces to detect children in images. Algorithm for classification of images into ...children and adults is developed and existing algorithms are analysed. This algorithm will also be used for age estimation. Through analysis of the state of the art researchon facial landmarks for age estimationand combination with changes that occur in human face morphology during growth and aging, facial landmarks needed for age classification and estimation of humans are identified. Algorithm is based on ratios of Euclidean distances between those landmarks. Based on these ratios, children can be detected and age can be estimated.
Classification of face images can be done in various ways. This research uses two-dimensional photographs of people's faces to detect children in images. Algorithm for classification of images into children and adults is developed and existing algorithms are analysed. This algorithm will also be used for age estimation. Through analysis of the state of the art researchon facial landmarks for age estimationand combination with changes that occur in human face morphology during growth and aging, facial landmarks needed for age classification and estimation of humans are identified. Algorithm is based on ratios of Euclidean distances between those landmarks. Based on these ratios, children can be detected and age can be estimated.
Slike lica mogu biti klasificirane na različite načine. Ovo istraživanje koristi dvodimenzionalne fotografije ljudskih lica za detekciju djece na slikama. Kreiran je novi algoritam za klasifikaciju fotografija ljudskih lica u dvije grupe, djeca i odrasli. Algoritam će se također koristiti za procjenu dobi osoba na slici te će biti analizirani postojeći algoritmi. Kroz analizu literature o karakterističnim točkama korištenih u procjeni dobi i kombinacijom dobivenih karakterističnih točaka s morfološkim promjenama tokom odrastanja i starenja, definirane su karakteristične točke potrebne za klasifikaciju i procjenu dobi. Algoritam se bazira na omjerima Euklidskih udaljenosti između identificiranih karakterističnih točaka.
Background/Objectives
Australians are more exposed to higher solar UV radiation levels that accelerate signs of facial ageing than individuals who live in temperate northern countries. The severity ...and course of self‐reported facial ageing among fair‐skinned Australian women were compared with those living in Canada, the UK and the USA.
Methods
Women voluntarily recruited into a proprietary opt‐in survey panel completed an internet‐based questionnaire about their facial ageing. Participants aged 18–75 years compared their features against photonumeric rating scales depicting degrees of severity for forehead, crow's feet and glabellar lines, tear troughs, midface volume loss, nasolabial folds, oral commissures and perioral lines. Data from Caucasian and Asian women with Fitzpatrick skin types I–III were analysed by linear regression for the impact of country (Australia versus Canada, the UK and the USA) on ageing severity for each feature, after controlling for age and race.
Results
Among 1472 women, Australians reported higher rates of change and significantly more severe facial lines (P ≤ 0.040) and volume‐related features like tear troughs and nasolabial folds (P ≤ 0.03) than women from the other countries. More Australians also reported moderate to severe ageing for all features one to two decades earlier than US women.
Conclusions
Australian women reported more severe signs of facial ageing sooner than other women and volume‐related changes up to 20 years earlier than those in the USA, which may suggest that environmental factors also impact volume‐related ageing. These findings have implications for managing their facial aesthetic concerns.
Facial ageing modelling has been an active research topic in the field of anthropology. Considering the fact that ageing is a non-uniform and a non-linear process for different face types (e.g. ...origins, gender etc.), dealing with a reliable face-ageing model may considerably help investigators working in some specific fields such as forensics. Unlike numerous studies dealing with forward or predictive face models, in this study, the authors propose a backward model aiming at estimating childhood face images using their corresponding adult face appearance as an input. For the proposed approach, face contour and different components are modified non-linearly, based on an estimated geometrical model. On the other hand, the face texture is estimated by mapping a reference face texture to the estimated geometrical model. This approach will show that it will be possible to ‘digitally’ rejuvenate an adult person's face down to it being 3–4 years old. For evaluation purposes, a database has been created from 112 subjects. Results have been evaluated using both objective (face recognition system) and subjective (human perception) criteria. The most promising and interesting results will be highlighted further ahead.
Cette thèse a pour objectif de modéliser, par approches biométriques, l’évolution dans le temps du visage humain, en partant de l’âge enfant, jusqu’à un âge adulte. Ces travaux sur le vieillissement ...rentrent dans le cadre des activités de recherche du groupe biométrie du laboratoire LiSSi (UPEC).Comme il est connu, l’évolution des traits dues au vieillissement dépend deplusieurs facteurs intrinsèques ou extrinsèques, dont : la génétique, l’origine ethnique, le mode de vie, etc. En considérons les modèles paramétriques proposés dans cette thèse, nous exploitons entre autres, les similitudes des caractéristiques extraites chez des individus d’une même catégorie d’âge. Ces similitudes sont intégrées dans nos modèles afin de pouvoir estimer l’apparence faciale à un âge spécifique. Contrairement aux nombreuses études traitant les modèles prédictifs de vieillissement facial, cette thèse propose pour la première fois un modèle réversible permettant également le rajeunissement numérique de l’apparence du visage que nous appellerons, modèle de prédiction arrière d’apparence. Quant à la prédiction avant, notre contribution s’est orientée vers la proposition d’un modèle non-linaire paramétrique de vieillissement permettant de prendre en considération les facteurs accélérateurs de vieillissements liés au mode de vie des individus. De manière générale, nous nous sommes intéressés aux conséquences de certaines addictions de type (drogues, alcool,exposition au soleil, etc.), sur le vieillissement prématuré du visage. Par conséquent,nous avons proposé des modèles sensibles à certains de ces facteurs en se basant sur des analyses statistiques. Comme retombés socio-économiques, cette étude a pour objectif de sensibiliser les jeunes personnes par rapport aux dangers liés à la consommation excessives de certaines substances, voire à l’addiction à certaines pratiques.Les études que nous avons menées durant cette thèse, ont nécessité la constitution d’une base de données contenant plus de 1600 images faciales. Cette base de données a permis le développement 30 modèles de visages «Face Templates». Suite à cela, nous avons créé une base de données d’évaluation, appelée «Face Time-Machine (FaceTiM)». Constituée à partir de 120 sujets, cette base de données est mise à disposition des chercheurs afin qu’ils puissent reproduire les résultats que nous avons obtenus, évaluer les performances, et enfin contribuer à l’amélioration des modèles proposés
The main focus of this thesis is to model the evolution trajectory of human face from infancy to senility using the biometrics facial features.The manifestation of facial changes caused by ageing depends on different factors such as genetic, ethnicity and lifestyle. Nevertheless, individuals in the same age group share some facial similarities. These resemblances can be employed to approximate the facial appearance of an individual in the bygone or the forthcoming years.Unlike numerous studies dealing with predictive face ageing models, for the first time, this thesis proposes the first Backward Facial Ageing Model aiming at digitally rejuvenate an adult face appearance down to its early childhood. We also present the Forward Facial Ageing Model to predict the adult face appearance in its future by taking into account the naturalageing trajectory. The main purpose of Forward Facial Ageing Model is to have a base model for the supplementary ageing models such as behavioural models.In this thesis for the first time in face ageing studies, the effects of different lifestyle behaviours are integrated into the facial ageing models. The Behavioural Facial Ageing Models predict the feature of a young face in case of having the high-risk lifestyle habits. The main attempt of these models is to illustrate the adverse effects of unsafe lifestyle behaviourson the senility of the face, aiming to prevent the youth from becoming involved in these habits. The Facial Ageing Modeling Database, contains over 1600 facial images, is collected to construct the models and 30 Face Templates for the purpose of the face ageing studies.Besides, the Face Time-Machine Database from 120 subjects is created and published to testand evaluate the results. For the proposed approach face contour and different components are modified non-linearly based on an estimated geometrical model related to the trajectory of growth or ageing. Moreover, the face texture is adapted by mapping a Face Template to the estimated geometrical model. Then, the effects of each lifestyle habit are set up to the primal predictive model.The evaluations of the results indicate that the proposed models are remarkably accurate to estimate the correct face appearance of an individual in the target age. While the simulated facial images are realistic and have the appearance, geometrical and textural characteristics of the target age, the personal identity and details of the input face images are preserved
PDF
