Age estimation from facial images based on Gabor feature fusion and the CIASO-SA algorithm

Aiming at the problem of long time-consuming and low accuracy of existing age estimation approaches,a new age estimation method using Gabor feature fusion,and an improved atomic search algorithm for feature selection is proposed.Firstly,texture features of five scales and eight directions in the face region are extracted by Gabor wavelet transform.The statistical histogram is introduced to encode and fuse the directional index with the largest feature value on Gabor scales.Secondly,a new hybrid feature selection algorithm chaotic improved atom search optimisation with simulated annealing(CIASO-SA)is presented,which is based on an improved atomic search algorithm and the simulated annealing algorithm.Besides,the CIASO-SA algorithm introduces a chaos mechanism during atomic initialisation,significantly improving the convergence speed and accuracy of the algorithm.Finally,a support vector machine(SVM)is used to get classification results of the age group.To verify the performance of the proposed algorithm,face images with three resolutions in the Adience dataset are tested.Using the Gabor real part fusion feature at 48�48 resolution,the average accuracy and 1-off accuracy of age classification exhibit a maximum of 60.4%and 85.9%,respectively.Obtained results prove the superiority of the proposed algorithm over the state-of-the-art methods,which is of great referential value for application to the mobile terminals.

Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling

Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster cases.Traditionally,this process is done manually by human expert.However,the speed and accuracy may vary depending on the expertise level of the human expert and other human factors such as level of fatigue and attentiveness.To improve the recognition speed and consistency,researchers have proposed automated age estimation using deep learning techniques such as Convolutional Neural Network(CNN).CNN requires many training images to obtain high percentage of recognition accuracy.Unfortunately,it is very difficult to get large number of samples of dental images for training the CNN due to the need to comply to privacy acts.A promising solution to this problem is a technique called Generative Adversarial Network(GAN).GAN is a technique that can generate synthetic images that has similar statistics as the training set.A variation of GAN called Conditional GAN(CGAN)enables the generation of the synthetic images to be controlled more precisely such that only the specified type of images will be generated.This paper proposes a CGAN for generating new dental images to increase the number of images available for training a CNN model to perform age estimation.We also propose a pseudolabelling technique to label the generated images with proper age and gender.We used the combination of real and generated images to trainDentalAge and Sex Net(DASNET),which is a CNN model for dental age estimation.Based on the experiment conducted,the accuracy,coefficient of determination(R2)and Absolute Error(AE)of DASNET have improved to 87%,0.85 and 1.18 years respectively as opposed to 74%,0.72 and 3.45 years when DASNET is trained using real,but smaller number of images.

Dental Age Estimation Based on X-ray Images

Chronological age estimation using panoramic dental X-ray images is an essential task in forensic sciences.Various statistical approaches have proposed by considering the teeth and mandible.However,building automated dental age estimation based on machine learning techniques needs more research efforts.In this paper,an automated dental age estimation is proposed using transfer learning.In the proposed approach,features are extracted using two deep neural networks namely,AlexNet and ResNet.Several classifiers are proposed to perform the classification task including decision tree,k-nearest neighbor,linear discriminant,and support vector machine.The proposed approach is evaluated using a number of suitable performance metrics using a dataset that contains 1429 dental X-ray images.The obtained results show that the proposed approach has a promising performance.

Face Age Estimation Based on CSLBP and Lightweight Convolutional Neural Network

As the use of facial attributes continues to expand,research into facial age estimation is also developing.Because face images are easily affected by factors including illumination and occlusion,the age estimation of faces is a challenging process.This paper proposes a face age estimation algorithm based on lightweight convolutional neural network in view of the complexity of the environment and the limitations of device computing ability.Improving face age estimation based on Soft Stagewise Regression Network(SSR-Net)and facial images,this paper employs the Center Symmetric Local Binary Pattern(CSLBP)method to obtain the feature image and then combines the face image and the feature image as network input data.Adding feature images to the convolutional neural network can improve the accuracy as well as increase the network model robustness.The experimental results on IMDB-WIKI and MORPH 2 datasets show that the lightweight convolutional neural network method proposed in this paper reduces model complexity and increases the accuracy of face age estimations.

Accuracy of Patient Age Estimation from Frontal Chest Radiographs of Adult Patients

Objective: The objective of this study is to evaluate the accuracy of patient age estimation from frontal chest radiographs of adult patients. Methods: 195 posterior-anterior chest radiographs without significant abnormalities were shown to 5 staff radiologists and 6 radiology residents, who were asked to provide their estimates of patient age to the nearest decade. Real patient age distribution ranged from 16 to 91 years of age. Results: On average, correct estimate of patient age decade was made in 22% of cases. Staff radiologists were overall more accurate in their estimations compared to residents. Best accuracy was achieved by the radiologist with the most years of clinical experience, however overall accuracy did not tend to correlate with number of years in practice for staff, nor years of post-graduate training for residents. Overall, patient age was most often overestimated. The least accurate estimates were made for patients younger than 20 years and older than 90. Best accuracy was seen for patients between 50 and 70 years of age. For patients between 20 and 90 years of age, overall estimates were within 11 - 15 years of their true age. There was no significant difference in accuracy of age estimation between radiographs of women and men. Conclusions: Average rate of correct age estimation to the nearest decade from normal frontal chest radiographs in our study was 22%. Staff radiologists were more accurate than radiology residents. Best estimates were made for middle-aged patients, and worst for extremes of age.

Coordinate-wise monotonic transformations enable privacy-preserving age estimation with 3D face point cloud

The human face is a valuable biomarker of aging,but the collection and use of its image raise significant privacy concerns.Here we present an approach for facial data masking that preserves age-related features using coordinate-wise monotonic transformations.We first develop a deep learning model that estimates age directly from non-registered face point clouds with high accuracy and generalizability.We show that the model learns a highly indistinguishable mapping using faces treated with coordinate-wise monotonic transformations,indicating that the relative positioning of facial information is a low-level biomarker of facial aging.Through visual perception tests and computational3D face verification experiments,we demonstrate that transformed faces are significantly more difficult to perceive for human but not for machines,except when only the face shape information is accessible.Our study leads to a facial data protection guideline that has the potential to broaden public access to face datasets with minimized privacy risks.

Age Estimation by Quantitative Cytomorphometric Study Using Buccal Mucosal Cells and Panoramic Radiography: A Comparative Forensic Study

Aim: This study aims to estimate the age of an individual using buccal cells by calculating the average cell size and comparing with pulp/tooth area ratio on mandibular canine and linear measurements related to mental foramen using panoramic radiography (PR). Materials and Methods: One hundred healthy individuals undergoing PR for various dental procedures were included in this study after obtaining consent. Pulp/tooth ratio using Cameriere et al. method on mandibular canine and linear measurements related to mental foramen, i.e., the distance between the superior and inferior border of the mandible (S-I) and the mandible’s superior border to the superior margin of the mental foramen (S-sM) were estimated using Adobe Photoshop image editing software. Later, buccal smears obtained from the same individual were fixed with 95% alcohol and stained with Papanicolaou’s stain for cytomorphometric analysis. The age was calculated using the regression analysis and correlated with chronological age. Results: Statistical significance and better correlation were obtained pulp/ tooth area ratio, the distance of S-I of all age groups. However, the distance of S-sM proved to be more effective in Group 4 (51-60 years) and buccal cell size was found to be comparable to the chronological age particularly in Group 5 (above 60 years). Conclusion: In the present study, the accuracy of estimated age compared to the chronological age was more promising for pulp/tooth area ratio, followed by S-I, S-sM, and cell size.

Progress in Age Estimation Based on DNA Methylation

In forensic practice,the age of suspects or victims is crucial information that aids in the resolution of cases.In recent years,age estimation based on DNA methylation has gained significant attention in forensic science.DNA methylation,an epigenetic marker,undergoes specific changes with age,making it a valuable tool for inferring the age of samples left at crime scenes.Therefore,the identification of age-related DNA methylation markers and the development of novel age estimation models are of great importance in forensic medicine.Numerous studies in the past decade have successfully established age estimation models based on DNA methylation,demonstrating excellent sensitivity and accuracy.To provide a comprehensive review,the authors of this paper conducted a systematic review of relevant articles published from 2012 to the present.We used keywords such as“forensic,”“DNA methylation,”and“age estimation”to retrieve pertinent articles from the Web of Science database.The review covers various aspects,including the sources of sample tissues used for age estimation,DNA methylation conversion methods,and different techniques for DNA methylation detection.In addition,the paper reviews the modeling methods for age estimation based on DNA methylation and factors that can influence DNA methylation.Overall,this review serves as a valuable reference for forensic genetics,offering insights into the latest advancements in age estimation using DNA methylation.As the field of forensic science continues to evolve,the integration of DNA methylation-based age estimation into practice is expected to enhance the accuracy and reliability of age determination in criminal investigations.

Practical age estimation using deep label distribution learning

Age estimation plays an important role in human-computer interaction system.The lack of large number of facial images with definite age label makes age estimation al-gorithms inefficient.Deep label distribution learning(DLDL)which employs convolutional neural networks(CNN)and label distribution learning to learn ambiguity from ground-truth age and adjacent ages,has been proven to outperform current state-of-the-art framework.However,DLDL assumes a rough label distribution which covers all ages for any given age label.In this paper,a more practical label distribution paradigm is proposed:we limit age label distribution that only covers a reasonable number of neighboring ages.In addition,we explore different label distributions to improve the performance of the proposed learning model.We employ CNN and the improved label distribution learning to estimate age.Experimental results show that compared to the DLDL,our method is more effective for facial age recognition.

Screening criteria of mRNA indicators for wound age estimation

Wound age estimation is a crucial and challenging problem in forensic pathology.Although mRNA is the most commonly used indicator for wound age estimation,screening criteria are lacking.In the present study,the feasibility of screening criteria using mRNA to determine injury time based on the adenylate-uridylate-rich element(ARE)structure and gene ontology(GO)categories were evaluated.A total of 78 Sprague-Dawley male rats were contused and sampled at 4,8,12,16,20,24,28,32,36,40,44,and 48 h after inflicting injury.The candidate mRNAs were classified based on with or without ARE structure and GO category function.The mRNA expression levels were detected using qRT-PCR.In addition,the standard deviation(STD),mean deviation(MD),relative average deviation(d%),and coefficient of variation(CV)were calculated based on mRNA expression levels.The CV score(CVs)and the CV of CV(CV’CV)were calculated to measure heterogeneity.Finally,based on classic principles,the accuracy of combination of candidate mRNAs was assessed using discriminant analysis to construct a multivariate model for inferring wound age.The results of homogeneity evaluation of each group based on CVs were consistent with the MD,STD,d%,and CV results,indicating the credibility of the evaluation results based on CVs.The candidate mRNAs without ARE structure and classified as cellular component(CC)GO category(ARE-CC)had the highest CVs,showing the mRNAs with these characteristics are the most homogenous mRNAs and best suited for wound age estimation.The highest accuracy was 91.0%when the mRNAs without ARE structure were used to infer the wound age based on the discrimination model.The accuracy of mRNAs classified into CC or multiple function(MF)GO category was higher than mRNAs in the biological process(BP)category.In all subgroups,the accuracy of the composite identification model of mRNA composition without ARE structure and classified as CC was higher than other subgroups.The mRNAs without ARE structure and belonging to the CC GO category were more homogenous,showed higher accuracy for estimating wound age,and were appropriate for rat skeletal muscle wound age estimation.

Comparing the accuracy of Demirjian and Nolla methods and establishing a new method for dental age estimation in northeastern Chinese children

Dental age estimation plays an important role in the field of clinic medicine and forensic medicine.The Demirjian and Nolla methods are common scoring methods for dental age estimation but there was no research about the comparison of accuracy of these two methods in northeastern Chinese children.Hence,in this study,we compared the accuracy of these two methods to explore more suitable method for our studied population.We collected 535 orthopantomograms from northern Chinese children aged from 6 to 15 years and divided them into training dataset and testing dataset according to the ratio of 7:3.The dental age of training dataset were estimated using Demirjian and Nolla methods,respectively.The results suggested that the mean differences of these two methods were 0.24 and−0.40 years,and mean absolute difference were 0.65 and 0.59 years.Then to further improve the accuracy of dental age assessment,the new improved formulas and dental age conversion tables were established after analyzing the relationship between the sum scores based on Nolla method and chronology age in training dataset.According to the new method used in testing dataset,the minimum value of mean difference(0.00)and mean absolute difference(0.49)were obtained,which are largely smaller than that of Demirjian and Nolla methods.The new developed method and dental age conversion scales may be more suitable dental age estimation method for northeastern Chinese children.

Can canines alone be used for age estimation in Chinese individuals when applying the Kvaal method?

Due to the secondary dentin formation,the dental pulp undergoes changes in shape throughout life.Based on this phenomenon,the Kvaal method has been applied to various populations for age estimation,and its usefulness has been verified.When applying the Kvaal method to Chinese subjects,we observed a relatively strong correlation between mandibular canines and age.This study notes the correlation between canines and chronological age and is the first to identify which canine is most closely related to chronological age.In addition,a new,simpler formula is determined based on canines according to Kvaal’s methodology.The radiographs of 360 individuals from northern China were selected,from which the widths and lengths of the pulp from four canines were measured according to the Kvaal method.Next,inter-and intra-observer reliabilities were analyzed in order to assess the repeatability of these measurements.The correlation between measurements and age was examined,and Chinese-specific age estimation formulae were derived.The results revealed that the ratios from the left maxillary canine exhibited the strongest correlation with age compared to the other canines,whereas the left mandibular canine showed the weakest correlation,which may contribute to the overall poor correlation of mandibular canines with age.What’s more,the formula derived from the left maxillary canine in this study displayed the highest coefficients of determination,and the formula derived from all canines showed the lowest residuals.Both of these formulae performed better than the Chinese-specific formula derived from six different types of teeth in our previous study,which had formerly possessed the highest coefficients of determination and the lowest residuals.Thus,we concluded that canines do play an important role in age estimation in the Chinese population,and the correlation between maxillary canines and chronological age is stronger than that of mandibular canines,although no distinct trend as to which side is better correlated with age was established.Going forward,we recommend the analysis of additional samples from different geographical regions and populations to further verify the importance of canines in age estimation.

Age estimation of individuals aged 5-23 years based on dental development of the Indonesian population

Dental development can be used to estimate age for forensic purposes.However,most of the currently available methods are less reliable for the Indonesian population due to population variability.This study presents a new method and evaluates other methods that utilize dental development to estimate the age of Indonesian people.Panoramic radiographs of 304 young Indonesian people aged 5–23 years old were analysed for deciduous tooth root resorption,permanent tooth calcification,and eruption.The extent of tooth root resorption was determined based on AlQahtani’s modified Moorrees et al.method.Tooth calcification was classified based on a modified Demirjian et al.method.Tooth eruption was evaluated based on AlQahtani’s modified Bengston system.The sequence of tooth root resorption,and permanent tooth calcification and eruption were grouped into 19 age categories(from 5–23 years old)in an atlas.The differences between males and females,between maxillary and mandibular teeth,and between right and left teeth were also analysed.There were minimal significant differences of tooth development between males and females,and between the right and left teeth(P>0.05),while the maxillary and mandibular dental development was significantly different(P<0.05).The newly developed atlas showed the development of the right side of maxillary and mandibular tooth of combined sex of Indonesian population.Another 34 panoramic radiographs of known-age and sex individuals from Indonesia were assessed using the newly developed Atlas of Dental Development in the Indonesian Population,Ubelaker’s Dental Development Chart,The London Atlas of Human Tooth Development and Eruption by AlQahtani,and the Age Estimation Guide-Modern Australia population by Blenkin-Taylor.Accuracy was assessed by comparing estimated age to actual chronological age using the Bland-Altmand test.Results show that the smallest range of error was found in the Atlas of Dental Development in the Indonesian Population(−0.969 to 1.210 years),followed by The London Atlas of Human Tooth Development and Eruption by AlQahtani(−2.013 to 1.990 years),the Age Estimation Guide-Modern Australia population by Blenkin-Taylor(−2.495 to 2.598 years),and the Dental Development Chart by Ubelaker(−2.960 to 3.289 years).These findings show that the Atlas of Dental Development constructed in this study performs better than the other three methods and presents greater accuracy of age estimation in the Indonesian population.

Application of Cameriere’s method for dental age estimation in children in South China

The aim of this study was to evaluate the applicability of Cameriere’s European formula for age estimation in children in South China and to adapt the formula to establish a more suitable formula for these children.Moreover,the performance of dental age estimation based on Cameriere’s method combining the developmental information of permanent teeth(PT)and third molar(TM)was also analysed.Orthopantomographs of 720 healthy children in Group A,and orthopantomographs of 320 children and 280 subadults in Group B were assessed.The samples of Group A were divided into training dataset 1 and test dataset 1,and the samples of Group B were also divided into training dataset 2 and test dataset 2.A South China-specific formula was established based on the training dataset 1,and the comparison of accuracy between the Cameriere’s European formula and the South China-specific formula was conducted with the test dataset 1.Additionally,a PT regression model,a TM regression model,and a combined regression model(PTþTM)were established based on the training dataset 2,and the performance of these three models were validated on the test dataset 2.The Cameriere’s European formula underestimated chronological age with a mean difference(ME)of-0.47±1.11 years in males and-0.69±1.19 years in females.However,the South China-specific formula underestimated chronological age,with a mean difference(ME)of-0.02±0.71 years in males and-0.14±0.73 years in females.Compared with PT model and TM model,the PT and TM combined model obtained the smallest root mean square error(RMSE)of 1.29 years in males and 0.93 years in females.In conclusion,the South China-specific formula was more suitable for assessing the dental age of children in South China,and the PT and TM combined model can improve the accuracy of dental age estimation in children.

A Three-Dimensional Real-Time Gait-Based Age Detection System Using Machine Learning

Human biometric analysis has gotten much attention due to itswidespread use in different research areas, such as security, surveillance,health, human identification, and classification. Human gait is one of the keyhuman traits that can identify and classify humans based on their age, gender,and ethnicity. Different approaches have been proposed for the estimation ofhuman age based on gait so far. However, challenges are there, for which anefficient, low-cost technique or algorithm is needed. In this paper, we proposea three-dimensional real-time gait-based age detection system using a machinelearning approach. The proposed system consists of training and testingphases. The proposed training phase consists of gait features extraction usingthe Microsoft Kinect (MS Kinect) controller, dataset generation based onjoints’ position, pre-processing of gait features, feature selection by calculatingthe Standard error and Standard deviation of the arithmetic mean and bestmodel selection using R2 and adjusted R2 techniques. T-test and ANOVAtechniques show that nine joints (right shoulder, right elbow, right hand, leftknee, right knee, right ankle, left ankle, left, and right foot) are statisticallysignificant at a 5% level of significance for age estimation. The proposedtesting phase correctly predicts the age of a walking person using the resultsobtained from the training phase. The proposed approach is evaluated on thedata that is experimentally recorded from the user in a real-time scenario.Fifty (50) volunteers of different ages participated in the experimental study.Using the limited features, the proposed method estimates the age with 98.0%accuracy on experimental images acquired in real-time via a classical generallinear regression model.

Exploring Brain Age Calculation Models Available for Alzheimer's Disease

The advantages of structural magnetic resonance imaging(sMRI)-based multidimensional tensor morphological features in brain disease research are the high sensitivity and resolution of sMRI to comprehensively capture the key structural information and quantify the structural deformation.However,its direct application to regression analysis of high-dimensional small-sample data for brain age prediction may cause“dimensional catastrophe”.Therefore,this paper develops a brain age prediction method for high-dimensional small-sample data based on sMRI multidimensional morphological features and constructs brain age gap estimation(BrainAGE)biomarkers to quantify abnormal aging of key subcortical structures by extracting subcortical structural features for brain age prediction,which can then establish statistical analysis models to help diagnose Alzheimer’s disease and monitor health conditions,intervening at the preclinical stage.

Leading directions and effective distance of larch offspring dispersal at the upper treeline in the Northern and Polar Urals, Russia

Climate has changed sufficiently over the last 150 years and forced out upper treeline advance at the most studied sites around the world.The rate of advance has been extremely variable–from tens to hundreds meters in altitude.This is because the degree at which tree frontal populations respond to climate change depends on the complex interaction of biological and physical factors.The resulting stand pattern is the consequence of the interaction between dispersal and survival functions.A few publications have addressed the question of how this pattern is generated.In order to understand how the spatial structure of tree stands was formed at the upper limit of their distribution in the Ural Mountains,we assessed the distance and direction of dispersal of offspring from maternal individuals.We found that in frontal Larix sibirica Ledeb.populations,‘effective’dispersal of offspring ranges from 3 to 758 m(with a median of 20–33 m in open forest and 219 m in single-tree tundra in the Polar Urals and 107 m in open forest in the Northern Urals).We revealed that most of the offspring effectively dispersed not only in the direction of the prevailing winds,but also in the opposite direction up the slope,and the distance can reach 500–760 m.The data obtained can be used to develop an individual-based model which is capable of simulating in detail the dynamics of tree stands at the upper limit of their growth and reliably predicting the future position and pattern of treeline ecotone as growth conditions continue to improve in the face of observed climate change.

Third-Molar Development in Relation to Chronologic Age in Young Adults of Central China

The estimation of chronologic age based on the stages of third-molar development was evaluated by using the eight stages （A–H） method of Demirjian and the third-molar development was compared, in terms of sex and age, with results of previous studies. The samples consisted of 291 orthopantomograms from young Chinese subjects of known chronologic age and sex （including 139 males with a mean age of 14.67±3.62 y and 152 females with a mean age of 14.85±3.70 y）. Statistical analysis was performed by employing the Mann-Whitney U-test and the t-test. Regression analysis was conducted to obtain regression formulas for calculating dental age from the chronologic age. Our results showed statistically significant differences （P〈0.05） in third-molar development between males and females, at the calcification stages D, E and H. And a strong correlation was found between age and third-molar development in both males （r2=0.65） and females （r2=0.61）. New equations （Age=8.76＋1.32 Development stage） for estimating chronologic age were derived. It is concluded that third-molar genesis took place earlier in males than in females. The use of third molars as a developmental marker is appropriate in young adults of Central China. The formula obtained in the present study can be used as a guide for estimation of dental maturity and a standard for age estimation for young adults of Central China.

Estimating time-based instantaneous total mortality rate based on the age-structured abundance index

The instantaneous total mortality rate(Z) of a fish population is one of the important parameters in fisheries stock assessment. The estimation of Z is crucial to fish population dynamics analysis,abundance and catch forecast,and fisheries management. A catch curve-based method for estimating time-based Z and its change trend from catch per unit effort(CPUE) data of multiple cohorts is developed. Unlike the traditional catch-curve method,the method developed here does not need the assumption of constant Z throughout the time,but the Z values in n continuous years are assumed constant,and then the Z values in different n continuous years are estimated using the age-based CPUE data within these years. The results of the simulation analyses show that the trends of the estimated time-based Z are consistent with the trends of the true Z,and the estimated rates of change from this approach are close to the true change rates(the relative differences between the change rates of the estimated Z and the true Z are smaller than 10%). Variations of both Z and recruitment can affect the estimates of Z value and the trend of Z. The most appropriate value of n can be different given the effects of different factors. Therefore,the appropriate value of n for different fisheries should be determined through a simulation analysis as we demonstrated in this study. Further analyses suggested that selectivity and age estimation are also two factors that can affect the estimated Z values if there is error in either of them,but the estimated change rates of Z are still close to the true change rates. We also applied this approach to the Atlantic cod(G adus morhua) fishery of eastern Newfoundland and Labrador from 1983 to 1997,and obtained reasonable estimates of time-based Z.

Using physical examinations to estimate age in elementary school children:A Chinese population-based study

Background:Designing a simple and accessible approach to age estimation in children and youth is a great challenge in the fields of sports and physical activity(PA).This study was designed to develop and validate a physical-examination-based method of estimating age in young children.Methods:In a cross-sectional study conducted in 2014,we performed physical examinations and assessed PA among 14,970 elementary school children 7–12 years old in Shanghai,China.Additional biological information on the children's height and birth date was ascertained through their parents.Two indicators were applied to develop a gender-specific age estimation equation:The percentage of predicted mature height(PPMH) and the Tanner stage.The equation was validated through a k-fold cross-validation approach.To check for estimation accuracy,the association between the discrepancy of estimated age(EA) and chronological age and PA was examined.Results:The gender-specific equations of EA were as follows:EA_(boy) = –6.071 + 6.559 Tanner 2 + 13.315 Tanner 3 + 14.130 Tanner 4 + 0.190 PPMH – 0.071 Tanner 2 × PPMH – 0.146 Tanner 3 × PPMH – 0.155 Tanner 4 × PPMH;EA_(girl) = –4.524 – 1.251 Tanner 2 + 2.504 Tanner 3 + 8.752 Tanner 4 + 11.893 Tanner 5 + 0.158 PPMH + 0.017 Tanner 2 × PPMH – 0.024 Tanner 3 × PPMH – 0.087 Tanner 4 × PPMH – 0.118 Tanner5 × PPMH.The mean absolute error was 0.60 years for boys and 0.59 years for girls.The discrepancy score was negatively and weakly associated with self-reported moderate-to-vigorous PA in both genders(r_(boy) = –0.09,p < 0.001;r_(girl) = –0.12,p < 0.001).Conclusion:Findings suggest that physical examinations could provide a valid and reliable approach for estimating age in young Chinese children.