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Robust Active Shape Model via Hierarchical Feature Extraction with SFS-Optimized Convolution Neural Network for Invariant Human Age Classification

Title
Robust Active Shape Model via Hierarchical Feature Extraction with SFS-Optimized Convolution Neural Network for Invariant Human Age Classification
Author
김기범
Issue Date
2021-02
Publisher
MDPI
Citation
ELECTRONICS, v. 10, No. 4, Article no. 465, 24pp
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
URI
https://www.mdpi.com/2079-9292/10/4/465https://repository.hanyang.ac.kr/handle/20.500.11754/166972
ISSN
2079-9292
DOI
10.3390/electronics10040465
Appears in Collections:
ETC[S] > 연구정보
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