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Journal articles on the topic 'Age estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

KRYVENCHUK, Yurii, and Roman YAKYMIV. "CREATION OF AGE ESTIMATION SYSTEM." Herald of Khmelnytskyi National University. Technical sciences 309, no. 3 (May 26, 2022): 66–69. http://dx.doi.org/10.31891/2307-5732-2022-309-3-66-69.

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We are surrounded by different transmitting and processing information devices on a daily basis. Analysis of certain selected characteristics of each person allows humanity to achieve previously unthinkable technological development. In particular, the human face. We can predict a person’s age, determine his mood or identify someone in the crowd using modern algorithms. As a “window to the soul”, the human face provides important information related to its individual traits. With the help of this information, a person can determine such characteristics as ethnic origin, gender, age, and emotions, on the basis of which he is able to take appropriate action, which plays a significant role in non-verbal communication between people. Age estimation is useful in applications where you do not need to specifically identify a person, but we want to know (approximate) their age. With the help of the age approximation system, it is possible to ensure partial digitalization of security control and monitoring systems. With the help of a monitoring camera, the age estiomation system can identify minors and prevent them from entering places where they are not allowed; not to allow minors to buy tobacco products or alcohol from vending machines; to warn the elderly person of possible danger. Age assessment software can also be used in healthcare systems. The authors personally are interested in approximating human age based on images to personalize human-machine interaction. The paper has the description and the results of the creation of an age estimation system, the basis of which is a convolutional neural network. The user can connect with the system through a simple interface which allows you to upload the image for analysis or turn on the camera to get age estimates in real-time. The practical value of this work is the developed high-quality system which is ready for use and implementation in the relevant fields.

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2

Liversidge, Helen M., Jo Buckberry, and Nicholas Marquez-Grant. "Age estimation." Annals of Human Biology 42, no. 4 (July 4, 2015): 299–301. http://dx.doi.org/10.3109/03014460.2015.1089627.

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3

Schmeling, A., G. Geserick, W. Reisinger, and A. Olze. "Age estimation." Forensic Science International 165, no. 2-3 (January 2007): 178–81. http://dx.doi.org/10.1016/j.forsciint.2006.05.016.

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4

Nanjundan, G. "A Note on the Estimation of the Age of a Galton-Watson Process." Mapana - Journal of Sciences 2, no. 1 (November 6, 2003): 38–42. http://dx.doi.org/10.12723/mjs.3.4.

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Nayak, Dr Shruti D., Dr Renjith George, Dr Amarnath Shenoy, and Dr B. Shivapathasundaram Dr. B. Shivapathasundaram. "Age Estimation in Forensic Dentistry- A Review." International Journal of Scientific Research 3, no. 4 (June 1, 2012): 333–38. http://dx.doi.org/10.15373/22778179/apr2014/117.

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6

Zhang, Huiying, Xin Geng, Yu Zhang, and Fanyong Cheng. "Recurrent age estimation." Pattern Recognition Letters 125 (July 2019): 271–77. http://dx.doi.org/10.1016/j.patrec.2019.05.002.

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7

Lanitis, Andreas. "Facial Age Estimation." Scholarpedia 5, no. 1 (2010): 9701. http://dx.doi.org/10.4249/scholarpedia.9701.

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Verma, Vivek, Dilip C. Nath, and S. N. Dwivedi. "Bayesian estimation of fertility rates under imperfect age reporting." Statistics in Transition new series 24, no. 2 (March 15, 2023): 39–57. http://dx.doi.org/10.59170/stattrans-2023-019.

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This article outlines the application of the Bayesian method of parameter estimation to situations where the probability of age misreporting is high, leading to transfers of an individual from one age group to another. An essential requirement for Bayesian estimation is prior distribution, derived for both perfect and imperfect age reporting. As an alternative to the Bayesian methodology, a classical estimator based on the maximum likelihood principle has also been discussed. Here, the age misreporting probability matrix has been constructed using a performance indicator, which incorporates the relative performance of estimators based on age when reported correctly instead of misreporting. The initial guess of performance indicators can either be empirically or theoretically derived. The method has been illustrated by using data on Empowered Action Group (EAG) states of India from National Family Health Survey-3 (2005–2006) to estimate the total marital fertility rates. The present study reveals through both a simulation and real-life set-up that the Bayesian estimation method has been more promising and reliable in estimating fertility rates, even in situations where age misreporting is higher than in case of classical maximum likelihood estimates.

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9

Kimura, Kunihiko. "Estimation of age at death from second metacarpals." Zeitschrift für Morphologie und Anthropologie 79, no. 2 (October 30, 1992): 169–81. http://dx.doi.org/10.1127/zma/79/1992/169.

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10

Hu, Zhenzhen, Yonggang Wen, Jianfeng Wang, Meng Wang, Richang Hong, and Shuicheng Yan. "Facial Age Estimation With Age Difference." IEEE Transactions on Image Processing 26, no. 7 (July 2017): 3087–97. http://dx.doi.org/10.1109/tip.2016.2633868.

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Lai, Dehe, Yewang Chen, Xiangyu Luo, Jixiang Du, and Tian Wang. "Age estimation with dynamic age range." Multimedia Tools and Applications 76, no. 5 (February 15, 2016): 6551–73. http://dx.doi.org/10.1007/s11042-015-3230-0.

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Hidayat, Septian Rahmat, Fahmi Oscandar, Yuti Malinda, Inne Suherna Sasmita, Murnisari Dardjan, Nani Murniati, and Yurika Ambar Lita. "Human age estimation based on pulp volume of canines for chronological age estimation: Preliminary research." Padjadjaran Journal of Dentistry 30, no. 3 (November 30, 2018): 184. http://dx.doi.org/10.24198/pjd.vol30no3.19302.

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Introduction: Age estimation has an important role in the process of human identification and needs to be performed in an efficient, fast, and accurate ways. Age estimation based on the pulp cavity volume of canines was referring to the theory stated that the apposition of secondary dentine during human life could be determined as an age indicator. This study was aimed to estimate the human age based on the pulp volume of canines for chronological estimation. Methods: Forty-one canine teeth from Cone Beam Computed Tomography (CBCT) 3D image sample from Dental Hospital of the Faculty of Dentistry Universitas Padjadjaran segmented into dental pulp and enamel were prepared for measurement of pulp volume using the ITK SNAP volumetric software. Descriptive analysis of pulp volume and regression equations were generated from regression analysis of pulp volume and tested for age estimation. Result: Dentine thickness was found to be increased from adolescence age range until adulthood age range. Logarithmic regression between pulp volume was statistically significant (p < 0,001). The coefficient of determination (R2) was 0.753 with a standard deviation of 8.421. Mathematics model for estimating age was: Age = 79.523 – (18.194 x ln(Pulp Volume)). The pulp cavity and dentine thickness were decreasing along the age. The same equation was also used in the same sample. There were several differences between chronological age and estimated age approximately 8.4 years following the regression result of R2 (75.3%). The rest of the sample (24.7%) have a big difference, and this could be affected by modifying variables such as occlusal force and human error measurement procedures. Conclusion: The human age can be estimated by the pulp volume of canines.Keywords: Age estimation, pulp volume, canine, ITK-SNAP, CBCT.

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Shang, Yanjie, Yakai Feng, Lipin Ren, Xiangyan Zhang, Fengqin Yang, Changquan Zhang, and Yadong Guo. "Pupal Age Estimation of Sarcophaga peregrina (Diptera: Sarcophagidae) at Different Constant Temperatures Utilizing ATR-FTIR Spectroscopy and Cuticular Hydrocarbons." Insects 14, no. 2 (January 31, 2023): 143. http://dx.doi.org/10.3390/insects14020143.

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Sarcophaga peregrina (Robineau-Desvoidy, 1830) (Diptera: Sarcophagidae) is a forensically important flesh fly that has potential value in estimating the PMImin. The precise pupal age estimation has great implications for PMImin estimation. During larval development, the age determination is straightforward by the morphological changes and variation of length and weight, however, the pupal age estimation is more difficult due to anatomical and morphological changes not being visible. Thus, it is necessary to find new techniques and methods that can be implemented by standard experiments for accurate pupal age estimation. In this study, we first investigated the potential of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and cuticular hydrocarbons (CHCs) for the age estimations of S. peregrina pupae at different constant temperatures (20 °C, 25 °C, and 30 °C). The orthogonal projections latent structure discrimination analysis (OPLS-DA) classification model was used to distinguish the pupae samples of different developmental ages. Then, a multivariate statistical regression model, partial least squares (PLS), was established with the spectroscopic and hydrocarbon data for pupal age estimations. We identified 37 CHCs with a carbon chain length between 11 and 35 in the pupae of S. peregrina. The results of the OPLS-DA model show a significant separation between different developmental ages of pupae (R2X > 0.928, R2Y > 0.899, Q2 > 0.863). The PLS model had a satisfactory prediction with a good fit between the actual and predicted ages of the pupae (R2 > 0.927, RMSECV < 1.268). The results demonstrate that the variation tendencies of spectroscopy and hydrocarbons were time-dependent, and ATR-FTIR and CHCs may be optimal for the age estimations of pupae of forensically important flies with implications for PMImin estimation in forensic practice.

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Karaarslan, Bekir, Emine Sirin Karaarslan, Abdul Semih Ozsevik, and Ertan Ertas. "Age Estimation for Dental Patients Using Orthopantomographs." European Journal of Dentistry 04, no. 04 (October 2010): 389–94. http://dx.doi.org/10.1055/s-0039-1697858.

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Objectives: The aim of this study was to conduct age estimates for dental patients using orthopantomographs (OPGs). The OPGs were selected by an independent author with respect to criteria and evaluated by two independent dentists. The results were compared to chronologic ages. The reliability of the estimates, concurrently made by the two independent dentists using OPGs, was also evaluated.Methods: In this retrospective study, the OPGs of 238 Turkish individuals of known chronological age, ranging from 1 to 60 years, were measured. Patients were then classified. Radiographs were evaluated by two independent dentists and age estimation was achieved according to the decades.Results: The truest age estimates made by the dentists were in the 1-10 years age range (89.6%), the most inaccurate age estimates were in the 41-50 years age range (41.7%). Results indicate that the accuracy of age estimation diminishes with age.Conclusions: Despite the variations related to the practitioners, in this study, there were no significant differences in age estimations between the two participant practitioners. Age estimation through evaluating OPGs was the most accurate in the first decade and the least in fourth decade. It can be concluded that OPGs are not adequate for accurate age estimation. (Eur J Dent 2010;4:389-394)

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English, Donald B. K., Eric M. White, J. M. Bowker, and Susan A. Winter. "A Review of the Forest Service's National Visitor Use Monitoring (NVUM) Program." Agricultural and Resource Economics Review 49, no. 1 (November 11, 2019): 64–90. http://dx.doi.org/10.1017/age.2019.27.

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The National Visitor Use Monitoring (NVUM) program data underlies estimates of the volume of recreation use of the National Forest System. The data also enable estimation of both the local economic contributions and nonmarket benefits of that visitation. Applications include evaluating the effects of natural disasters, site characteristics, and climate change, as well as expenditure and benefit transfers. This article describes the history and science background of the NVUM program, outlines the methods used in estimating market and nonmarket economic outcomes, and lists some examples of results found in the literature.

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16

Saputri, Rosalina Intan. "Dental Age Estimation of Indonesian Population : A Literature Review." SONDE (Sound of Dentistry) 5, no. 1 (April 8, 2020): 13–21. http://dx.doi.org/10.28932/sod.v5i1.2380.

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Age is one of the important parts of the identification process, which is an requirement in legal framework, criminal investigation, and, in health care, to provide significant information for diagnosis and treatment planning. Age estimation is a major study in forensic, especially when the information related to the deceased is unavailable. Dental is one of the strong variables which could be used in estimating the age of living or deceased. Dental age estimation methods were mainly based on the changes in tooth development which influenced by diverse internal and external factor. Therefore, studies of dental age estimation of various population were resulted different discrepancies. This paper reviews about studies of dental age estimation specifically in Indonesian population, including the population age, age estimation methods, study sample, and studies’ findings.

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17

KAYA, Kenan, and Necmi ÇEKİN. "Age Estimation in Children." Turkiye Klinikleri Journal of Forensic Medicine and Forensic Sciences 15, no. 1 (2018): 24–28. http://dx.doi.org/10.5336/forensic.2018-59773.

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18

Ginsburg, N. "Numerosity Estimation and Age." Perceptual and Motor Skills 79, no. 3_suppl (December 1994): 1434. http://dx.doi.org/10.2466/pms.1994.79.3f.1434.

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19

Demirkıran, D. Sümeyra. "Methods for age estimation." Dicle Medical Journal/Dicle Tıp Dergisi 41, no. 1 (March 1, 2014): 238–43. http://dx.doi.org/10.5798/diclemedj.0921.2014.01.0410.

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20

VESTLUND, JENNY, LINDA LANGEBORG, PATRIK SÖRQVIST, and MÅRTEN ERIKSSON. "Experts on age estimation." Scandinavian Journal of Psychology 50, no. 4 (August 2009): 301–7. http://dx.doi.org/10.1111/j.1467-9450.2009.00726.x.

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21

Whitehouse, William. "Estimation of gestational age." Lancet 341, no. 8842 (February 1993): 440–41. http://dx.doi.org/10.1016/0140-6736(93)93038-3.

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22

Miller, Geoffrey. "Estimation of gestational age." Pediatric Neurology 4, no. 2 (March 1988): 129. http://dx.doi.org/10.1016/0887-8994(88)90058-6.

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23

Drusini, Andrea G. "Age estimation from teeth using soft X-ray findings." Anthropologischer Anzeiger 51, no. 1 (March 16, 1993): 41–46. http://dx.doi.org/10.1127/anthranz/51/1993/41.

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24

Mishra, Ramkrishna, Vinay Kumar Srivastava, Surendra Kumar Pandey, and Naveen Kumar PG. "Dental Age Estimation using Demirjian's Methods: A Comparative study." Indian Journal of Forensic Medicine and Pathology 15, no. 4 (December 15, 2022): 263–74. http://dx.doi.org/10.21088/ijfmp.0974.3383.15422.9.

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context: There are various methods have been developed for dental age estimation, of these; Demirjian’s seven teeth method is the most tested and accepted method worldwide. It is based on tooth development rather than tooth eruption, because the developmental process is strictly controlled by genes and less inﬂuenced by dietary insufficiency, endocrinal disturbances, and environmental insults. aims: The purpose of this study is to evaluate the applicability of two Demirjian’s methods in a sample of the Varanasi region. setting and design: This is a cross-sectional prospective study. material and methods: The evaluation of digital panoramic images of a total of 432 children and adolescents (237 boys and 195 girls) of age rang 3-16 years from the population of the Varanasi region. Seven left mandibular teeth excluding the third molar were rated and scored to obtain dental age using Dem73 and Dem76 methods. statistical analysis: Paired t-test was applied to determine the significant diﬀerence between estimated dental age and chronological age and correlation was established using Pearson’s correlation coefficient. results: The Dem73 method overestimated the dental age in boys by 0.23 (±0.80) years and by 0.12 (±0.85) years in the total sample. In the girls’ sample, there was no discernible diﬀerence in mean age. In boys, girls, and the total sample, the Dem76 method overestimated dental ages by 0.85 (±1.14), 0.45 (±1.00), and 0.67 (±1.10) years, respectively. Pearson’s correlation revealed a high coefficient value between dental and chronological ages. conclusions: Dem73 method is more applicable than the Dem76 method for age estimation among children and adolescents in the Varanasi region.

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Maunder, Mark N., and Richard B. Deriso. "Estimation of recruitment in catch-at-age models." Canadian Journal of Fisheries and Aquatic Sciences 60, no. 10 (October 1, 2003): 1204–16. http://dx.doi.org/10.1139/f03-104.

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Management strategies must be designed to take into account the uncertainty inherent in fish populations and their assessments. Annual recruitment variation is an important component of uncertainty. Several methods that allow the estimation of annual recruitment in statistical catch-at-age models are described: (a) maximum likelihood estimation with no penalty on the annual recruitment residuals, (b) maximum likelihood estimation with a lognormal penalty on the annual recruitment residuals, (c) importance sampling to numerically approximate the marginal likelihood with a lognormal penalty on the annual recruitment residuals, and (d) full Bayesian integration using Markov Chain Monte Carlo with a lognormal prior on the annual recruitment residuals. Simulation analysis is used to test the performance of these methods. All four methods perform similarly at estimating quantities that are based on averaging or summing multiple estimates of annual recruitment; however the marginal likelihood method (c) and Bayesian integration (d) perform best at estimating annual recruitment and the standard deviation in annual recruitment residuals (σR) when catch-at-age data is missing for some years. The ability to estimate σR can be important for defining uncertainty when developing management strategies. The methods are applied to a New Zealand snapper (Pagrus auratus) stock and the estimate of σR is approximately 0.6.

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Zhang, Yifei, Jinping Bi, Yao Ning, and Jiang Feng. "Methodology Advances in Vertebrate Age Estimation." Animals 14, no. 2 (January 22, 2024): 343. http://dx.doi.org/10.3390/ani14020343.

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Age is a core metric in vertebrate management, and the correct estimation of the age of an individual plays a principal role in comprehending animal behavior, identifying genealogical information, and assessing the potential reproductive capacity of populations. Vertebrates have a vertebral column and a distinct head containing a developed brain; they have played an important role in the study of biological evolution. However, biological age estimations constantly exhibit large deviations due to the diversity of vertebrate taxon species, sample types, and determination methods. To systematically and comprehensively understand age estimation methods in different situations, we classify the degree of damage to vertebrates during sample collection, present the sample types and their applications, list commonly applied methods, present methodological recommendations based on the combination of accuracy and implementability, and, finally, predict future methods for vertebrate age assessments, taking into account the current level of research and requirements. Through comprehensive data gathering and compilation, this work serves as an introduction and summary for those who are eager to catch up on related fields and facilitates the rapid and accurate selection of an evaluation method for researchers engaged in related research. This is essential to promote animal conservation and guide the smooth implementation of conservation management plans.

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Stanley, Thomas R. "Estimating Stage-Specific Daily Survival Probabilities of Nests When Nest age is Unknown." Auk 121, no. 1 (January 1, 2004): 134–47. http://dx.doi.org/10.1093/auk/121.1.134.

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Abstract Estimation of daily survival probabilities of nests is common in studies of avian populations. Since the introduction of Mayfield's (1961, 1975) estimator, numerous models have been developed to relax Mayfield's assumptions and account for biologically important sources of variation. Stanley (2000) presented a model for estimating stage-specific (e.g. incubation stage, nestling stage) daily survival probabilities of nests that conditions on “nest type” and requires that nests be aged when they are found. Because aging nests typically requires handling the eggs, there may be situations where nests can not or should not be aged and the Stanley (2000) model will be inapplicable. Here, I present a model for estimating stage-specific daily survival probabilities that conditions on nest stage for active nests, thereby obviating the need to age nests when they are found. Specifically, I derive the maximumlikelihood function for the model, evaluate the model's performance using Monte Carlo simulations, and provide software for estimating parameters (along with an example). For sample sizes as low as 50 nests, bias was small and confidence interval coverage was close to the nominal rate, especially when a reduced-parameter model was used for estimation.

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Bolancé, Catalina, and Montserrat Guillen. "Nonparametric Estimation of Extreme Quantiles with an Application to Longevity Risk." Risks 9, no. 4 (April 15, 2021): 77. http://dx.doi.org/10.3390/risks9040077.

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A new method to estimate longevity risk based on the kernel estimation of the extreme quantiles of truncated age-at-death distributions is proposed. Its theoretical properties are presented and a simulation study is reported. The flexible yet accurate estimation of extreme quantiles of age-at-death conditional on having survived a certain age is fundamental for evaluating the risk of lifetime insurance. Our proposal combines a parametric distributions with nonparametric sample information, leading to obtain an asymptotic unbiased estimator of extreme quantiles for alternative distributions with different right tail shape, i.e., heavy tail or exponential tail. A method for estimating the longevity risk of a continuous temporary annuity is also shown. We illustrate our proposal with an application to the official age-at-death statistics of the population in Spain.

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Williams, Brett, Malcolm Boyle, and Peter O'Meara. "Can Undergraduate Paramedic and Nursing Students Accurately Estimate Patient Age and Weight?" Prehospital and Disaster Medicine 25, no. 2 (April 2010): 171–77. http://dx.doi.org/10.1017/s1049023x00007937.

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Introduction:Accurate estimation of a patient's age and weight are skills expected of all healthcare clinicians, including paramedics and nurses. It is necessary because patients may be unable to communicate such information due to unconsciousness or an altered state of conscious. Age and weight estimation influence calculation for medication dosages, defibrillation, equipment sizing, and other invasive procedures such as intubation. The objective of this study was to identify whether undergraduate paramedic and nursing students were able to accurately estimate a patient's age and weight based on digital patient photos.Methods:A prospective, observational study involving undergraduate paramedic and nursing students from two Australian universities was used to estimate the age and weight of seven patients (adult and pediatric). Each patient image appeared in a PowerPointTMpresentation for 15 seconds, followed by a short pause, with the next patient image commencing automatically.Results:The findings demonstrated variable accuracy in age and weight estimation of the patients. Age estimations of pediatric patients were more accurate than estimations for adult patients. The majority of patient weights were under-estimated, with university undergraduate students in one university displaying similar estimations to the other university counterparts.Conclusions:Results from this study identified variations in students' ability to accurately estimate a patient's age and weight. This study shows that consideration should be given to age and weight estimation education, which could be incorporated into undergraduate healthcare curriculum.

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Solheim, Tore, and Anne Vonen. "Dental age estimation, quality assurance and age estimation of asylum seekers in Norway." Forensic Science International 159 (May 2006): S56—S60. http://dx.doi.org/10.1016/j.forsciint.2006.02.016.

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31

Demetrescu, Traian Cristian, and Cristian Oliviu Burada. "Estimation of Construction Depreciation by the Age-of-Life Method." Applied Mechanics and Materials 880 (March 2018): 365–70. http://dx.doi.org/10.4028/www.scientific.net/amm.880.365.

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Estimation of construction depreciation is an important element in the valuation of real estate by cost approach. The age method - economic life is one of the three methods of estimating the total depreciation of the buildings used by the authorized evaluators in Romania. The two elements used in estimating total depreciation are actual age and economic life. For estimating the economic lifetime a quantitative method can be used using market data.

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Liao, Haibin, Yuchen Yan, Wenhua Dai, and Ping Fan. "Age Estimation of Face Images Based on CNN and Divide-and-Rule Strategy." Mathematical Problems in Engineering 2018 (June 5, 2018): 1–8. http://dx.doi.org/10.1155/2018/1712686.

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In recent years, the research on age estimation based on face images has drawn more and more attention, which includes two processes: feature extraction and estimation function learning. In the aspect of face feature extraction, this paper leverages excellent characteristics of convolution neural network in the field of image application, by using deep learning method to extract face features, and adopts factor analysis model to extract robust features. In terms of age estimation function learning, age-based and sequential study of rank-based age estimation learning methods is utilized and then a divide-and-rule face age estimator is proposed. Experiments in FG-NET, MORPH Album 2, and IMDB-WIKI show that the feature extraction method is more robust than traditional age feature extraction method and the performance of divide-and-rule estimator is superior to classical SVM and SVR.

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Kask, Kristjan, and Mariliis Tael-Öeren. "How Young/Old Does One Look? Sales Personnel’s and Laypersons’ Estimation of Young People’s Age." Juridica International 25 (November 5, 2017): 82. http://dx.doi.org/10.12697/ji.2017.25.09.

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Several studies have found that the level of alcohol use among minors both in Europe generally and in Estonia is relatively high. However, we have less knowledge of issues related to age estimations in this field. Therefore, research was conducted to examine how accurate sales personnel in Estonia are in estimating the age of young people and, in addition, to compare salespersons and laypersons (i.e., persons not working in shops that sell food and alcohol) with regard to their ability to make accurate age estimations. For this purpose, 20 salespersons and 20 laypersons participated in an experiment in which they estimated the age of people whose faces were presented to them in images. Salespersons’ estimation of young persons’ age from the photos was more accurate than laypersons’ estimation. However, both groups tended to overestimate the age of the people shown, especially when the focus was on the difference between minors of age 17 and young adults of age 18 or 19. It can be concluded that accurately discriminating between minors’ and adults’ faces by using only facial cues is difficult. One solution for addressing this issue in practice would be to raise the age threshold for asking for ID. While many shops already pursue this approach, it is on a voluntary basis; in Estonia, there is no legal requirement to do so.

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Vanishri, Perugu, and Nishat Ahmed Sheikh. "Stature Estimation from Foot Length of School Age Group Children." Indian Journal of Forensic Medicine and Pathology 10, no. 1 (2017): 11–16. http://dx.doi.org/10.21088/ijfmp.0974.3383.10117.2.

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35

Patyal, Shama, and Tejasvi Pandey. "Estimation of age from digital chest radiographs in district Mandi, Himachal Pradesh." Problems of Forensic Sciences, no. 132 (May 8, 2023): 229–39. http://dx.doi.org/10.4467/12307483pfs.22.012.17685.

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In forensic investigations estimation of age is a crucial part. Age estimation assists in narrow downing the search possibilities in the case of unidentified bodies and living individuals, it can also help in the determination of preparators and deciding the penalty for any criminal liability. Different body parts are often investigated at the crime scene, so estimating the age using different areas of the body is important. Hence, we conducted this study for age estimation from costal cartilage calcification. Only posterior-anterior (PA) digital chest radiographs are taken, and the first rib of the left side is evaluated. The degree of calcification and age showed a positive correlation; similarly, the value of Spearman’s rho showed significant results (for the whole sample =0.914, p < 0.01 (for male subjects was =0.925, p < 0.01. For females was =0.905, p < 0.01). The degree of calcification is useful in estimating age groups and becomes more determined with advancing age. Prediction of age using the radiological method gives the advantages of ease, inexpensiveness, rapidity, and high accuracy. In developing countries, this type of study will be very useful because infrastructure and lack of sources are major issues. The article provides a non-destructive method of age estimation in a forensic context that may act as a preliminary method of age estimation in mass disasters and dismembered bodies when a thoracic cage is only present.

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Deng, Yulan, Shaohua Teng, Lunke Fei, Wei Zhang, and Imad Rida. "A Multifeature Learning and Fusion Network for Facial Age Estimation." Sensors 21, no. 13 (July 5, 2021): 4597. http://dx.doi.org/10.3390/s21134597.

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Age estimation from face images has attracted much attention due to its favorable and many real-world applications such as video surveillance and social networking. However, most existing studies usually learn a single kind of age feature and ignore other appearance features such as gender and race, which have a great influence on the age pattern. In this paper, we proposed a compact multifeature learning and fusion method for age estimation. Specifically, we first used three subnetworks to learn gender, race, and age information. Then, we fused these complementary features to further form more robust features for age estimation. Finally, we engineered a regression-ranking age-feature estimator to convert the fusion features into the exact age numbers. Experimental results on three benchmark databases demonstrated the effectiveness and efficiency of the proposed method on facial age estimation in comparison to previous state-of-the-art methods. Moreover, compared with previous state-of-the-art methods, our model was more compact with only a 20 MB memory overhead and is suitable for deployment on mobile or embedded devices for age estimation.

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Campanacho, Vanessa, Andrew T. Chamberlain, Pia Nystrom, and Eugénia Cunha. "Degenerative variance on age-related traits from pelvic bone articulations and its implication for age estimation." Anthropologischer Anzeiger 77, no. 3 (August 12, 2020): 243–58. http://dx.doi.org/10.1127/anthranz/2020/1184.

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Gunjal, Aishwarya, and Deepa Abin. "Survey on Age Estimation System." International Journal of Computer Applications 182, no. 3 (July 16, 2018): 1–4. http://dx.doi.org/10.5120/ijca2018917478.

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Gallant, Roy, Tara Fidler, and Kim A. Dawson. "Subjective Time Estimation and Age." Perceptual and Motor Skills 72, no. 3_suppl (June 1991): 1275–80. http://dx.doi.org/10.2466/pms.1991.72.3c.1275.

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Böhme, Petra, Alexandra Reckert, Julia Becker, and Stefanie Ritz-Timme. "Molecular methods for age estimation." Rechtsmedizin 31, no. 3 (April 29, 2021): 177–82. http://dx.doi.org/10.1007/s00194-021-00490-9.

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41

Alkass, Kanar, Bruce A. Buchholz, Susumu Ohtani, Toshiharu Yamamoto, Henrik Druid, and Kirsty L. Spalding. "Age Estimation in Forensic Sciences." Molecular & Cellular Proteomics 9, no. 5 (December 4, 2009): 1022–30. http://dx.doi.org/10.1074/mcp.m900525-mcp200.

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Vaishnavi, M. S., and A. Vijayalakshmi. "Age Estimation Using OLPP Features." Oriental journal of computer science and technology 10, no. 1 (March 23, 2017): 238–48. http://dx.doi.org/10.13005/ojcst/10.01.33.

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Aging face recognition poses as a key difficulty in facial recognition. It refers to identification of a person face over varied ages. It includes issues like age estimation, progression and verification. Non-availability of facial aging databases make it harder for any system to achieve good accuracy as there are no good training sets available. Age estimation when done correctly has a varied number of real life applications like age detailed vending machines, age specific access control and finding missing children. This paper implements age estimation using Park Aging Mind laboratory - Face database that contains metadata and 293 unique images of 293 individuals. Ages range from 19 to 45 with a median age of 32. Race is classified into two categories : African-American and Caucasian giving an accuracy of 98%. Sobel edge detection and Orthogonal locality preservation projection were used as the dominant features for the training and testing of age estimation. A Multi-stage binary classification using support vector machine was used to classify images into an age group thereafter predicting an individual’s age. The effectiveness of this method can be increased by using a large dataset with a wider age range.

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Lu, Jiwen, and Yap-Peng Tan. "Gait-Based Human Age Estimation." IEEE Transactions on Information Forensics and Security 5, no. 4 (December 2010): 761–70. http://dx.doi.org/10.1109/tifs.2010.2069560.

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Venkatapathy, Ramesh, Sankari Radhakrishnan, Pennagaram D. Balamurali, Premalatha Babu, Karthik SV Prasad, and Mohamed Thawfeek. "Exfoliative Cytology in Age Estimation." Journal of Scientific Dentistry 9, no. 2 (2019): 33–35. http://dx.doi.org/10.5005/jp-journals-10083-0903.

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Lucy, D., R. G. Aykroyd, and A. M. Pollard. "Nonparametric calibration for age estimation." Journal of the Royal Statistical Society: Series C (Applied Statistics) 51, no. 2 (May 2002): 183–96. http://dx.doi.org/10.1111/1467-9876.00262.

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Schmeling, Andreas, Andreas Olze, Walter Reisinger, and Gunther Geserick. "Forensic age estimation and ethnicity." Legal Medicine 7, no. 2 (March 2005): 134–37. http://dx.doi.org/10.1016/j.legalmed.2004.07.004.

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Timme, Maximilian, Jürgen Michael Steinacker, and Andreas Schmeling. "Age estimation in competitive sports." International Journal of Legal Medicine 131, no. 1 (October 14, 2016): 225–33. http://dx.doi.org/10.1007/s00414-016-1456-7.

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Schmeling, A., W. Reisinger, G. Geserick, and A. Olze. "Age estimation of unaccompanied minors." Forensic Science International 159 (May 2006): S61—S64. http://dx.doi.org/10.1016/j.forsciint.2006.02.017.

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Olze, A., W. Reisinger, G. Geserick, and A. Schmeling. "Age estimation of unaccompanied minors." Forensic Science International 159 (May 2006): S65—S67. http://dx.doi.org/10.1016/j.forsciint.2006.02.018.

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Meissner, Christoph, and Stefanie Ritz-Timme. "Molecular pathology and age estimation." Forensic Science International 203, no. 1-3 (December 2010): 34–43. http://dx.doi.org/10.1016/j.forsciint.2010.07.010.

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