Automatic age estimation based on facial aging patterns

  • 90% (561 votes)
  • 12.10.2018
In recent years, face recognition technology has become a hot topic in the field of pattern recognition. The human face is one of the most important human biometric characteristics, which contains a lot of important information, such as identity, gender, age, expression, race and so on. Human age is a significant reference for identity discrimination, and age estimation can be potentially applied in human-computer interaction, computer vision and business intelligence. This paper addresses the problem of accurate estimation of human age. An age estimation system is generally composed of aging feature extraction and feature classification. In the feature extraction part, well-known texture descriptors like the Gabor wavelets and the Local Binary Patterns LBP have been utilized for the feature extraction.
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Age estimation via face images: a survey

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Learning from facial aging patterns for automatic age estimation - Semantic Scholar

Facial aging adversely impacts performance of face recognition and face verification and authentication using facial features. This stochastic personalized inevitable process poses dynamic theoretical and practical challenge to the computer vision and pattern recognition community. Age estimation is labeling a face image with exact real age or age group. How do humans recognize faces across ages? Do they learn the pattern or use age-invariant features? What are these age-invariant features that uniquely identify one across ages? These questions and others have attracted significant interest in the computer vision and pattern recognition research community.
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Automatic Human Age Estimation Using Overlapped Age Groups

Computer Vision, Imaging and Computer Graphics. Theory and Application pp Cite as. Facial aging effects can be perceived in two main forms; the first one is the growth related transformations and the second one is the textural variations. Therefore, in order to generate an efficient age classifier, both shape and texture information should be used together.
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Skip to search form Skip to main content. However, automatic age estimation technique is still underdeveloped. One of the main reasons is that the aging effects on human faces present several unique characteristics which make age estimation a challenging task that requires non-standard classification approaches. View on ACM.
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automatic age estimation based on facial aging patterns

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