A study of the facial aging - a multidisciplinary approach

Leta, Fabiana Rodrigues; Pamplona, Djenane; Weber, Hans Ingo; Conci, Aura; Pitanguy, Ivo

doi:10.1590/s0100-73862000000300009

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…They referred to cranio -facial research, the article makeup, plastic surgery. The publicly available FG-Net aging database is commonly used in many previous works for age estimation in order to evaluate performance [ 6,7,8,9,10].The recent texture based approaches [11,12,13,14,15,16] Morph Data base [18] and the percentage of success is about 91.86. V V Kumar et al, [19] proposed Topological Texture Features (TTF).…”

Section: Related Work In Age Estimationmentioning

confidence: 99%

Age Group Estimation Based on the Transition Count of 3rd Order Neighborhood using V and Inverted V Patterns

Devi*,

Babu

2019

IJRTE

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Age Classification is used in so many applications like crime detection, face detection and so on. . The age leads to significant variation in human face. The variation depends on many factors like gender, exposure to sunlight, drinking, weight loss or weight gain. In our paper the performance of face aging is established based on v pattern and Inverted v pattern by using the transition count of third order neighborhood. In our proposed method the age of the person is divided into 5 categories 1.Childhood (0-12years) 2.Young Adults (13-25years) 3.Middle Age Adults (26-40years) 4.Senior Adults (40-60years) 5.Senior Citizens (more than 60 years).The quantative evaluation and analysis is performed in our proposed method when compared to other existing methods after applying on 4 different facial image databases

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Section: Related Work In Age Estimationmentioning

confidence: 99%

Age Group Estimation Based on the Transition Count of 3rd Order Neighborhood using V and Inverted V Patterns

Devi*,

Babu

2019

IJRTE

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“…So, most researchers study shape and texture changes simultaneously in adult aging modeling. Large varieties of functions are proposed, including quadratic function [24], [32], [27], support vector regression [22], [56], [45], kernel smoothing method [20], or an implicit function [6], [30], [28], [46]. Differently, Jiang and Wang [21] directly built a mapping function between a young face and its appearance in later ages.…”

Section: Function-based Approachesmentioning

confidence: 99%

A Concatenational Graph Evolution Aging Model

Suo

Chen²,

Shan³

et al. 2012

IEEE Trans. Pattern Anal. Mach. Intell.

101

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Modeling the long-term face aging process is of great importance for face recognition and animation, but there is a lack of sufficient long-term face aging sequences for model learning. To address this problem, we propose a CONcatenational GRaph Evolution (CONGRE) aging model, which adopts decomposition strategy in both spatial and temporal aspects to learn long-term aging patterns from partially dense aging databases. In spatial aspect, we build a graphical face representation, in which a human face is decomposed into mutually interrelated subregions under anatomical guidance. In temporal aspect, the long-term evolution of the above graphical representation is then modeled by connecting sequential short-term patterns following the Markov property of aging process under smoothness constraints between neighboring short-term patterns and consistency constraints among subregions. The proposed model also considers the diversity of face aging by proposing probabilistic concatenation strategy between short-term patterns and applying scholastic sampling in aging prediction. In experiments, the aging prediction results generated by the learned aging models are evaluated both subjectively and objectively to validate the proposed model.

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“…Leta et al [11,12] establish the correlation of 26 facial distance measurements with age so that it is possible to predict how the 26 distance measurements are modified, as a person grows older. Image warping techniques are used for modifying the shape of a face in order to inflict age-related shape deformations specified by the modification of the 26 distance measurements.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Evaluation of Automatic Age Progression Methodologies

Lanitis¹

2008

EURASIP Journal on Advances in Signal Processing

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Automatic age-progression is the process of modifying an image showing the face of a person in order to predict his/her future facial appearance. In this paper, we compare the performance of two age-progression methodologies reported in the literature against two novel approaches to the problem. In particular, we compare the performance of a method based on age prototypes, a method based on aging functions defined in a low-dimensional parametric model space, and two methods based on the distributions of samples belonging to different individuals and different age groups. Quantitative comparative results reported in the paper are based on dedicated performance evaluation metrics that assess the ability of each method to produce accurate predictions of the future/previous facial appearance of subjects. The framework proposed in this paper promotes the idea of a standardized performance evaluation protocol for age-progression methodologies, using images from a publicly available image database.

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A study of the facial aging - a multidisciplinary approach

Cited by 6 publications

References 3 publications

Age Group Estimation Based on the Transition Count of 3rd Order Neighborhood using V and Inverted V Patterns

Age Group Estimation Based on the Transition Count of 3rd Order Neighborhood using V and Inverted V Patterns

A Concatenational Graph Evolution Aging Model

Comparative Evaluation of Automatic Age Progression Methodologies

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