• Title/Summary/Keyword: age estimation

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The Social Analysis on the Age Estimation of Living Body in Jeollabuk-Do

  • Jung, Won;Suh, Bong-Jik
    • Journal of Oral Medicine and Pain
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    • v.43 no.4
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    • pp.118-124
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    • 2018
  • Purpose: Age estimation is often used in the identification of living persons. Various methods are used for age estimation using teeth, and there are many studies on the methodology. But the study of changes in the social aspects of age estimation with the passage of times is still insufficient. Therefore, the purpose of this study is to analyze the age estimation cases in the social aspects and to investigate the changes of age estimation cases in Jeollabuk-do. Methods: From January 2008 to December 2015, 76 cases of age estimation were collected. The collected data were organized and analyzed. The distribution of patients by age and year, the difference between alleged and registered age, the purpose of age estimation, and regional distribution were examined. In addition, we compared the previous study which analyzed the age estimation cases in Jeollabuk-do from 2000 to 2007. Results: According to the distribution by age, the age distribution was the largest in the 50s and 60s, with 69.8%. The most reason to correct age was related to welfare benefits (38.2%), and most of the people who corrected for welfare benefits were over 50 years old. The age correction for purpose of welfare benefits existed every year during the study period. As the result of comparison with previous study, total number of age estimation cases was decreased very sharply, and distribution by age group was also changed. Conclusions: Changes in age estimation cases were observed when compared to the previous study. A significant decrease in the total number of age estimation cases was observed, but the number of age estimation in the 50s did not decrease. Although the total number of age estimation requests decreases, age estimation in the elderly are likely to persist. Thus, it is necessary to study new age estimation methods suitable for the elderly.

Dental Radiography for Age Estimation: A Scoping Review

  • Jeon, Kug Jin;Kim, Young Hyun;Lee, Joo-Young;Jung, Hoi In;Han, Sang-Sun
    • Journal of Korean Dental Science
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    • v.15 no.1
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    • pp.31-50
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    • 2022
  • Purpose: This study was to investigate the types of imaging modalities, analytical methods for age estimation, and the age of the subjects in research on age estimation using dental radiography through a scoping review, and to investigate the overall trends in age estimation studies. Materials and Methods: A scoping review was designed according to the Arksey and O'Malley guidelines and the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) statement. Three electronic databases were used as search sources (Medline, Embase, and Cochrane Library). Studies were classified according to the three main components of the research question. "What are the imaging modalities, analytical methods, and target age in dental imaging-based age estimation studies?" Result: The final 198 studies were selected by two reviewers. The most common imaging modality used in studies was panoramic radiography (69.7%), and studies using cone-beam computed tomography have increased over time. Analytical methods for age estimation were 62.6% in studies based on tooth development and 26.3% in studies using pulp/tooth ratio. The subject age was 27.8% for children and 27.3% for adults. Studies conducted in all age groups comprised the smallest category (5.2%). Conclusion: Panoramic radiography has been the most used types of imaging modalities for age estimation, and the most common analytical method was analysis of tooth development. Most studies targeted specific ages, and very few involved all age groups. Dental age estimation studies should be carried out with appropriate consideration of the imaging modality that is analyzed, the methods that are used, and the age that is targeted.

A study of age estimation from occluded images (가림이 있는 얼굴 영상의 나이 인식 연구)

  • Choi, Sung Eun
    • Journal of Platform Technology
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    • v.10 no.3
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    • pp.44-50
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    • 2022
  • Research on facial age estimation is being actively conducted because it is used in various application fields. Facial images taken in various environments often have occlusions, and there is a problem in that performance of age estimation is degraded. Therefore, we propose age estimation method by creating an occluded part using image extrapolation technology to improve the age estimation performance of an occluded face image. In order to confirm the effect of occlusion in the image on the age estimation performance, an image with occlusion is generated using a mask image. The occluded part of facial image is restored using SpiralNet, which is one of the image extrapolation techniques, and it is a method to create an occluded part while crossing the edge of an image. Experimental results show that age estimation performance of occluded facial image is significantly degraded. It was confirmed that the age estimation performance is improved when using a face image with reconstructed occlusions using SpiralNet by experiments.

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Light-weight Gender Classification and Age Estimation based on Ensemble Multi-tasking Deep Learning (앙상블 멀티태스킹 딥러닝 기반 경량 성별 분류 및 나이별 추정)

  • Huy Tran, Quoc Bao;Park, JongHyeon;Chung, SunTae
    • Journal of Korea Multimedia Society
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    • v.25 no.1
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    • pp.39-51
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    • 2022
  • Image-based gender classification and age estimation of human are classic problems in computer vision. Most of researches in this field focus just only one task of either gender classification or age estimation and most of the reported methods for each task focus on accuracy performance and are not computationally light. Thus, running both tasks together simultaneously on low cost mobile or embedded systems with limited cpu processing speed and memory capacity are practically prohibited. In this paper, we propose a novel light-weight gender classification and age estimation method based on ensemble multitasking deep learning with light-weight processing neural network architecture, which processes both gender classification and age estimation simultaneously and in real-time even for embedded systems. Through experiments over various well-known datasets, it is shown that the proposed method performs comparably to the state-of-the-art gender classification and/or age estimation methods with respect to accuracy and runs fast enough (average 14fps) on a Jestson Nano embedded board.

The Study of Age Estimation from Tooth using the Racemization of Aminoacid (아미노산의 라세미화 반응을 이용한 치아로부터의 연령감정에 관한 연구)

  • Hee-Kyung Kim;Chong-Youl Kim
    • Journal of Oral Medicine and Pain
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    • v.14 no.1
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    • pp.43-55
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    • 1989
  • The need of age estimation for identification was increased by complexity of society, and the tooth was used widely for age estimation because of less individual deviation than the other organ. The age estimation using the tooth had several methods. Recently, the one using the racemization of aminoacid in the tooth was admitted more accurate than the other methods, especially in old age. But, this study was not tried in our country, and I would report the result of experiment about age estimation using racemization of dentine. I selected 40-Whole dentine sample from extracted teeth, those were reserved in natural dried condition for 2 weeks~ 1year and calculated the estimation of age from the ratio of D-aminoacid and L-aminoacid (D/L ratio) using gaschromatography and the results were below. 1. The aminoacids showed apparent K/L ratio in dentine were aspartic acid, serine. 2. The aspartic acid showed the highest racemic rate and its rate was 0.0012$\pm$0.0003/yr. 3. The relation between the actual age and K/L ratio was very positive correlation(r+0.954) in the estimation of age using aspartic acid. 4. The deviation between the estimated age using D/L ratio of aspartic acid and actual age was $\pm$3.32.

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Hierarchical Age Estimation based on Dynamic Grouping and OHRank

  • Zhang, Li;Wang, Xianmei;Liang, Yuyu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.7
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    • pp.2480-2495
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    • 2014
  • This paper describes a hierarchical method for image-based age estimation that combines age group classification and age value estimation. The proposed method uses a coarse-to-fine strategy with different appearance features to describe facial shape and texture. Considering the damage to continuity between neighboring groups caused by fixed divisions during age group classification, a dynamic grouping technique is employed to allow non-fixed groups. Based on the given group, an ordinal hyperplane ranking (OHRank) model is employed to transform age estimation into a series of binary enquiry problems that can take advantage of the intrinsic correlation and ordinal information of age. A set of experiments on FG-NET are presented and the results demonstrate the validity of our solution.

An Analysis of Age Estimation Cases in Korea from the View of Social Aspects (사회적 측면에서 본 한국 연령 감정 대상자 사례의 분석)

  • Kwon, Choonik;Byun, Jin-Seok;Jung, Jae-Kwang;Choi, Jae-Kap
    • Journal of Oral Medicine and Pain
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    • v.38 no.3
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    • pp.235-246
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    • 2013
  • Age estimations by forensic odontology play a great role in personal identification. The purpose of this study was to analyze the characteristics of age estimation cases in Korea. Surveying clients who requested age estimation at the Department of Oral Medicine, Kyungpook National University Dental Hospital from March 2012 to March 2013. The results were as follows: 1. On gender distribution, females(57.14%) were more than males(42.86%). The elderly with 50's and over 60's were majorities(89.28%) of clients, and no clients were below 40's. Most of clients were equal to and lower than elementary school graduate(69.64%). 2. The most frequent reason for age discrepancy between registered age and alleged age was mistakes by family or relatives(80.36%). The purposes of age estimation were welfare(62.50%), social relationship problem(12.50%), to find right age(10.71%), and occupation(8.93%). 3. In order of route to visit at Department of Oral Medicine, they were via government office(48.21%), acquaintances(21.43%), mass media(14.29%), and clinic(10.71%). Clients had high degree of comprehension on age estimation with forensic odontology (scored 7.03 out of 10). The 2/3 of clients were satisfied with present fee for age estimation. 4. The percentage on the proximity of estimated age to alleged age was 69.81%. 4(11.43%) clients were approved on age correction by court.

Study on the Face recognition, Age estimation, Gender estimation Framework using OpenBR. (OpenBR을 이용한 안면인식, 연령 산정, 성별 추정 프로그램 구현에 관한 연구)

  • Kim, Nam-woo;Kim, Jeong-Tae
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2017.05a
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    • pp.779-782
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    • 2017
  • OpenBR is a framework for researching new facial recognition methods, improving existing algorithms, interacting with commercial systems, measuring perceived performance, and deploying automated biometric systems. Designed to facilitate rapid algorithm prototyping, it features a mature core framework, flexible plug-in system, and open and closed source development support. The established algorithms can be used for specific forms such as face recognition, age estimation, and gender estimation. In this paper, we describe the framework of OpenBR and implement facial recognition, gender estimation, and age estimation using supported programs.

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Robust Deep Age Estimation Method Using Artificially Generated Image Set

  • Jang, Jaeyoon;Jeon, Seung-Hyuk;Kim, Jaehong;Yoon, Hosub
    • ETRI Journal
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    • v.39 no.5
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    • pp.643-651
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    • 2017
  • Human age estimation is one of the key factors in the field of Human-Robot Interaction/Human-Computer Interaction (HRI/HCI). Owing to the development of deep-learning technologies, age recognition has recently been attempted. In general, however, deep learning techniques require a large-scale database, and for age learning with variations, a conventional database is insufficient. For this reason, we propose an age estimation method using artificially generated data. Image data are artificially generated through 3D information, thus solving the problem of shortage of training data, and helping with the training of the deep-learning technique. Augmentation using 3D has advantages over 2D because it creates new images with more information. We use a deep architecture as a pre-trained model, and improve the estimation capacity using artificially augmented training images. The deep architecture can outperform traditional estimation methods, and the improved method showed increased reliability. We have achieved state-of-the-art performance using the proposed method in the Morph-II dataset and have proven that the proposed method can be used effectively using the Adience dataset.

Facial Age Estimation Using Convolutional Neural Networks Based on Inception Modules (인셉션 모듈 기반 컨볼루션 신경망을 이용한 얼굴 연령 예측)

  • Sukh-Erdene, Bolortuya;Cho, Hyun-chong
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.67 no.9
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    • pp.1224-1231
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    • 2018
  • Automatic age estimation has been used in many social network applications, practical commercial applications, and human-computer interaction visual-surveillance biometrics. However, it has rarely been explored. In this paper, we propose an automatic age estimation system, which includes face detection and convolutional deep learning based on an inception module. The latter is a 22-layer-deep network that serves as the particular category of the inception design. To evaluate the proposed approach, we use 4,000 images of eight different age groups from the Adience age dataset. k-fold cross-validation (k = 5) is applied. A comparison of the performance of the proposed work and recent related methods is presented. The results show that the proposed method significantly outperforms existing methods in terms of the exact accuracy and off-by-one accuracy. The off-by-one accuracy is when the result is off by one adjacent age label to the above or below. For the exact accuracy, the age label of "60+" is classified with the highest accuracy of 76%.