Senility of the Face—basic Study to Understand Its Causes and Effects

González-Ulloa, M; Flores, E S

doi:10.1097/00006534-196508000-00013

Cited by 153 publications

(68 citation statements)

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“…[21,[24][25][26][27][28][29][30][31][32][33] Age related changes to muscle structure and function have also been described. [34][35][36][37][38][39] Other researchers have studied the age related changes as they pertain to lip length and maxillary incisor exposure; however, to our knowledge this is the first study to examine a comprehensive list of smile parameters with respect to age.…”

Section: Discussionmentioning

confidence: 99%

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Dynamic smile analysis: Changes with age

Desai¹,

Upadhyay

Nanda

2009

American Journal of Orthodontics and Dentofacial Orthopedics

154

158

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Section: Discussionmentioning

confidence: 99%

“…Furthermore, with age, dermal alterations are also seen in collagen, elastin, and glycosaminoglycans. [24][25][26][27][28][29][30][31][32][33] Skin collagen has been shown to decrease linearly at a rate of approximately 1%…”

Section: Introductionmentioning

confidence: 99%

Dynamic smile analysis: Changes with age

Desai¹,

Upadhyay

Nanda

2009

American Journal of Orthodontics and Dentofacial Orthopedics

154

158

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“…1,2 With time, people undergo many skeletal and soft tissue cellular changes that dramatically affect the overlying soft tissue envelope, the related muscles, and their functions. 3,4 The literature demonstrates that with age, the lips become less elastic and less mobile. 5,6 Further, oral structures such as teeth and periodontium change with age.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of a smile in different age groups

Chetan

Tandon

Singh

et al. 2012

The Angle Orthodontist

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Objective: To evaluate smile in different age groups and to detect gender differences in smile. Materials and Methods: Digital videographic records of 241 randomly selected subjects were obtained for smile analysis. The subjects were divided into four groups by age (15-20 years, 21-30 years, 31-40 years, and 41-50 years). Each group was further subdivided by gender. After 41 subjects were excluded, the smile dimensions of 200 subjects were analyzed by two-way multivariate analysis of variance (MANOVA) with Duncan's multiple range post hoc test.

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“…[1]. Extent and frequency of facial expressions, emotional stress, exposure to sunlight, extreme weight loss, smoking, usage of anti-aging products, and plastic surgery also affect the person's facial appearance [2]. Therefore, determining the type of facial features that represents the age directly is very difficult.…”

Section: Introductionmentioning

confidence: 99%

Facial Age Estimation based on Decision Level Fusion of AAM, LBP and Gabor Features

Günay¹,

Nabiyev²

2015

ijacsa

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Abstract-In this paper a new hierarchical age estimation method based on decision level fusion of global and local features is proposed. The shape and appearance information of human faces which are extracted with active appearance models (AAM) are used as global facial features. The local facial features are the wrinkle features extracted with Gabor filters and skin features extracted with local binary patterns (LBP). Then feature classification is performed using a hierarchical classifier which is the combination of an age group classification and detailed age estimation. In the age group classification phase, three distinct support vector machines (SVM) classifiers are trained using each feature vector. Then decision level fusion is performed to combine the results of these classifiers. The detailed age of the classified image is then estimated in that age group, using the aging functions modeled with global and local features, separately. Aging functions are modeled with multiple linear regressions. To make a final decision, the results of these aging functions are also fused in decision level. Experimental results on the FG-NET and PAL aging databases have shown that the age estimation accuracy of the proposed method is better than the previous methods.

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Senility of the Face—basic Study to Understand Its Causes and Effects

Cited by 153 publications

References 0 publications

Dynamic smile analysis: Changes with age

Dynamic smile analysis: Changes with age

Dynamics of a smile in different age groups

Facial Age Estimation based on Decision Level Fusion of AAM, LBP and Gabor Features

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