Human age classification using facial skin aging features and artificial neural network

被引：22

作者：

Jagtap, Jayant ^{[1
]}

Kokare, Manesh ^{[1
]}

机构：

[1] Shri Guru Gobind Singhji Inst Engn & Technol, Dept Elect & Telecommun Engn, Vishnupuri 431606, Nanded, India

来源：

COGNITIVE SYSTEMS RESEARCH | 2016年 / 40卷

关键词：

Skin aging features; Artificial Neural Network (ANN); Local Gabor Binary Pattern Histogram (LGBPH); Wrinkle analysis; Age classification; RECOGNITION;

D O I：

10.1016/j.cogsys.2016.05.002

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper a novel method based on facial skin aging features and Artificial Neural Network (ANN) is proposed to classify the human face images into four age groups. The facial skin aging features are extracted by using Local Gabor Binary Pattern Histogram (LGBPH) and wrinkle analysis. The ANN classifier is designed by using two layer feedforward backpropagation neural networks. The proposed age classification framework is trained and tested with face images from PAL face database and shown considerable improvement in the age classification accuracy up to 94.17% and 93.75% for male and female respectively. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：116 / 128

页数：13

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