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Fusion for Image Based Human Age Estimation



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 real2010world 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.



Total Pages: 5
Pages: 319-323


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Volume: 23
Issue: 2
Year: 2016

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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."

Journal: 1995-Present


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