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Literature review on age estimation

27 7 months ago
Curator: Andreas Lanitis. Andreas Lanitis , Cyprus University of Technology. The main issues presented in the article involve typical applications where facial age estimation can be used, problem and challenges associated with facial age estimation, typical approaches reported in the literature and future research directions. The appearance of a human face is affected considerably by aging see Figure 1. Facial aging effects are mainly attributed to bone movement and growth and skin related deformations associated with the introduction of wrinkles and reduction of muscle strength Albert , Rhodes Usually bone growth takes place during childhood whereas during adult ages the most intense age-related deformations are linked with texture changes.
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3. noninvasive age estimation technique

Introduction: Age estimation is essential in human identification, but also in civil and pension lawsuits. Teeth maturation is better than other structures and the dental changes provide characteristics which are grouped in different age estimation methods. Objective: The purpose of this literature review was to present the main methods in age estimation that have been currently used. Just papers about dental age estimation methods written in English between and were selected. Results: 67 papers were retrieved through electronic searching, but nine studies were excluded. Conclusion: The most dental age estimation methods were based on developmental stages of the teeth through radiographs and they were applied in children and sub-adults in countries of the different continents. Age estimation plays an important role in Forensic Dentistry for dead individual identification as well as for alive persons to clarify criminal and civil liability issues [1].
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Evaluating Automated Facial Age Estimation Techniques For Digital Forensics

Metrics details. 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.
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Human characteristics, such as age, height, weight, gender, hair color, etc. Various automated machine learning-based techniques have been implemented for the analysis of digital images to detect soft biometric traits, such as age and gender, and thus aid detectives and investigators in progressing their cases. This paper documents an evaluation of existing cognitive age prediction services. The evaluative and comparative analysis of the various services was conducted to identify trends and issues inherent to their performance. One significant contributing factor impeding the accurate development of the services investigated is the notable lack of sufficient sample images in specific age ranges, i.
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