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A Framework for Age Estimation Using Facial Features: A Comprehensive Survey
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Author(s): A. Annie Micheal (Department of Computer Science and Engineering, Sathyabama Institute of Science and technology, Kanchipuram, India), B. U. Anu Barathi (Department of Computer Science and Engineering, Sathyabama Institute of Science and technology, Kanchipuram, India), A. Ancy Micheal (Department of School of Computer Science and Engineering, Vellore Institute of Technology (Chennai Campus), Chengalpattu, India)and Essakki Raja T. (Department of Computer Science and Engineering with Data Science,)
Copyright: 2025
Pages: 30
Source title:
Navigating Usability and User Experience in a Multi-Platform World
Source Author(s)/Editor(s): Anacleto Correia (CINAV, Naval Academy, Portugal)
DOI: 10.4018/979-8-3693-2337-3.ch009
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Abstract
In human age estimation, an exact age or age group is assigned automatically to a given facial image. Due to changes in facial features caused by wrinkles and muscle sag with the increase in age, age estimation becomes difficult and complex. Variability in facial expressions, illumination conditions, environment, pose, plastic surgery, occlusion, image quality, and facial makeup gives rise to some of the challenges that affect the accuracy of age estimation. With the newly developing real-time and age-related applications, there is an increasing demand for more accurate estimation of age. Several methodologies have been developed by researchers to handle this requirement. The work reported here represents an exhaustive survey on face age estimation. Discussed here are five major models of age image representation and various age learning algorithms, a review of the existing aging databases has been made, and the latest state-of-the-art methods for the estimation of age are summarized.
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