Abstract
The features and appearance of the human face are affected greatly by aging. A human face is an important aspect for human age identification from childhood through adulthood. Although many traits are used in human age estimation, this article discusses age classification using salient texture and facial landmark feature vectors. We propose a novel human age classification (HAC) model that can localize landmark points of the face. A robust multi-perspective view-based Active Shape Model (ASM) is generated and age classification is achieved using Convolution Neural Network (CNN). The HAC model is subdivided into the following steps: (1) at first, a face is detected using aYCbCr color segmentation model; (2) landmark localization is done on the face using a connected components approach and a ridge contour method; (3) an Active Shape Model (ASM) is generated on the face using three-sided polygon meshes and perpendicular bisection of a triangle; (4) feature extraction is achieved using anthropometric model, carnio-facial development, interior angle formulation, wrinkle detection and heat maps; (5) Sequential Forward Selection (SFS) is used to select the most ideal set of features; and (6) finally, the Convolution Neural Network (CNN) model is used to classify according to age in the correct age group. The proposed system outperforms existing statistical state-of-the-art HAC methods in terms of classification accuracy, achieving 91.58% with The Images of Groups dataset, 92.62% with the OUI Adience dataset and 94.59% with the FG-NET dataset. The system is applicable to many research areas including access control, surveillance monitoring, human–machine interaction and self-identification.
Highlights
Introduction published maps and institutional affilDespite changes in lifestyle and environment, the first signs of human facial aging show up between the ages of 25–30 years
Materials and Methods color segmentation model; second, 35 landmarks are specified on the face using the connected components and ridge contour methods; third, an Active Shape Model (ASM) is generated on the face using two approaches—three-sided polygon mesh and perpendicular bisection a triangle; fourth, feature extraction doneusing usingtwo twotechniques—image techniques—image bisection of a of triangle; fourth, feature extraction is is done representation consisting of a three sub-phase anthropometric model, carnio-facial develwhich opment and interior angle formulation and feature extraction using aging patterns which phases; wrinkles wrinkles detection detection and heat heat maps
Heat maps are an effective way to extract useful information which helps in accurate age classification
Summary
A lot of research has been carried out on the age classification of individuals and age groups using 2D and 3D images. Facial age estimation is divided into two groups. The first group is from infancy to adulthood when most changes occur; the second stage is from the teenage to old age where skin color, texture and elasticity changes are most likely to occur. Various age estimation methods have been investigated. Age estimation using multiple faces has been carried out by researchers. We discuss age classification using single and multi-face datasets
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