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
- Keywords
- Active Shape Model; Anthropometric model; Deep learning method; Face detection; Landmark localization; Sequential Forward Selection
- Issue Date
- 2021-02
- Publisher
- MDPI AG
- Citation
- Electronics (Switzerland), v. 10.0, NO. 4, article no. 465, Page. 1-24
- 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. Alt-hough 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- URI
- https://www.mdpi.com/2079-9292/10/4/465https://repository.hanyang.ac.kr/handle/20.500.11754/179191
- ISSN
- 2079-9292;2079-9292
- DOI
- 10.3390/electronics10040465
- Appears in Collections:
- COLLEGE OF COMPUTING[E](소프트웨어융합대학) > MEDIA, CULTURE, AND DESIGN TECHNOLOGY(ICT융합학부) > Articles
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