Fusion for Image Based Human Age Estimation. Issue 2 (3rd April 2017)

• Record Type: Journal Article
• Authors: Baskaran, Madhusudhanan; Kalpana, R.
• Is Part Of: Intelligent automation & soft computing. Volume 23:Issue 2(2017)
• Page Start: 319
• Page End: 323
• Publication Date: 2017-04-03
• Subjects: Human Face -- automatic human age estimation -- Support Vector Regression (SVR) -- Gray Level Co-occurrence Matrix (GLCM) -- Active Appearance Models (AAMs) -- Support Vector Regression (SVR) -- Cuckoo Search (CS); Artificial intelligence -- Periodicals; Intelligent control systems -- Periodicals; 003.5
• DOI: 10.1080/10798587.2016.1231471
• Languages: English
• ISSNs: 1079-8587

Abstract

Abstract: Human faces act as essential visual signal and reveal significant amounts of nonverbal information in human-to-human communication. In an automatic age estimation system, shape-based and texture-based features extracted from faces forms the basis of age estimation. Human age estimation with facial image analysis as an automated method houses many potential real‐world factors. This paper presents an automated age calculation framework with the help of Support Vector Regression (SVR) strategy and it highlights feature extraction with Gray Level Co-occurrence Matrix (GLCM), and Active Appearance Models (AAMs) to evaluate human age. A fused feature technique and a SVR based Cuckoo Search (CS) is proposed in order to reduce error in age estimation.