Approach For Age Estimation Research Articles

Masked contrastive graph representation learning for age estimation

Age estimation of face images is a crucial task with various practical applications in areas such as video surveillance and Internet access control. While deep learning-based age estimation methods like Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Transformers have shown remarkable performance, they have limitations when modeling complex or irregular objects, and often contain redundant information. Although existing graph neural networks (GNNs) can alleviate this issue, they only considers the structural information of the image and ignores the semantic features, thus the feature representation capability of graph nodes is limited. To address these issues, this paper proposes a novel Masked Contrastive Graph Representation Learning (MCGRL) method for accurate age estimation of face images. Our approach leverages CNN to extract semantic features of the image, which are then partitioned into patches that serve as nodes in the graph. We use a masked graph convolutional network (GCN) to derive image-based node representations that capture rich structural information. Finally, we incorporate multiple losses to explore the complementary relationship between structural information and semantic features, which improves the feature representation capability of GCN. Experimental results on real-world face image datasets demonstrate the superiority of our proposed method over other state-of-the-art age estimation approaches. Our code is available at https://github.com/yuntaoshou/MCGRL

Artificial intelligence and dental age estimation: development and validation of an automated stage allocation technique on all mandibular tooth types in panoramic radiographs.

Age estimation in forensic odontology is mainly based on the development of permanent teeth. To register the developmental status of an examined tooth, staging techniques were developed. However, due to inappropriate calibration, uncertainties during stage allocation, and lack of experience, non-uniformity in stage allocation exists between expert observers. As a consequence, related age estimation results are inconsistent. An automated staging technique applicable to all tooth types can overcome this drawback.This study aimed to establish an integrated automated technique to stage the development of all mandibular tooth types and to compare their staging performances.Calibrated observers staged FDI teeth 31, 33, 34, 37 and 38 according to a ten-stage modified Demirjian staging technique. According to a standardised bounding box around each examined tooth, the retrospectively collected panoramic radiographs were cropped using Photoshop CC 2021® software (Adobe®, version 23.0). A gold standard set of 1639 radiographs were selected (n31 = 259, n33 = 282, n34 = 308, n37 = 390, n38 = 400) and input into a convolutional neural network (CNN) trained for optimal staging accuracy. The performance evaluation of the network was conducted in a five-fold cross-validation scheme. In each fold, the entire dataset was split into a training and a test set in a non-overlapping fashion between the folds (i.e., 80% and 20% of the dataset, respectively). Staging performances were calculated per tooth type and overall (accuracy, mean absolute difference, linearly weighted Cohen's Kappa and intra-class correlation coefficient). Overall, these metrics equalled 0.53, 0.71, 0.71, and 0.89, respectively. All staging performance indices were best for 37 and worst for 31. The highest number of misclassified stages were associated to adjacent stages. Most misclassifications were observed in all available stages of 31.Our findings suggest that the developmental status of mandibular molars can be taken into account in an automated approach for age estimation, while taking incisors into account may hinder age estimation.

Accuracy of geometric morphometrics for age estimation using frontal face photographs of children and adolescents: A promising method for forensic practice

Age estimation is crucial in legal and humanitarian contexts. Forensic professionals may use various procedures to estimate age, including dental analysis, bone density tests, evaluation of physical characteristics including facial bone structure and development, and image-based methods. Although images are often the only material available, visual observation of photographic material is an imprecise method in age estimation, which can compromise judicial decision-making. Analyzing 4000 photographs from the Brazilian Federal Police database, representing four age groups (6, 10, 14, and 18 years), the study employed automated analysis by marking 28 photogrammetric points. Data were used to establish facial patterns by age and sex using the facial geometric morphometrics method. Performance was assessed through a Multinomial Logistic Regression model, evaluating accuracy, sensitivity, and specificity across the categorical age groups. Analyses were conducted using R software, with a 5 % significance level. The study found that facial geometric morphometrics achieved an overall accuracy of 69.3 % in age discrimination, with higher accuracy in males (74.7 %) compared to females (65.8 %) (p < 0.001). The method excelled at predicting the age of 6-year-olds with 87.3 % sensitivity and 95.6 % specificity but had lower performance at 14 years. It showed greater accuracy in distinguishing age groups with larger age gaps, achieving up to 99.5 % accuracy between certain groups, and was particularly effective in differentiating ages of 6 and 10 years in females and 10, 14, and 18 years in males. The facial geometric morphometrics emerges as a promising approach for age estimation among children and adolescents in forensic settings.

Leverage Effective Deep Learning Searching Method for Forensic Age Estimation.

Dental age estimation is extensively employed in forensic medicine practice. However, the accuracy of conventional methods fails to satisfy the need for precision, particularly when estimating the age of adults. Herein, we propose an approach for age estimation utilizing orthopantomograms (OPGs). We propose a new dental dataset comprising OPGs of 27,957 individuals (16,383 females and 11,574 males), covering an age range from newborn to 93 years. The age annotations were meticulously verified using ID card details. Considering the distinct nature of dental data, we analyzed various neural network components to accurately estimate age, such as optimal network depth, convolution kernel size, multi-branch architecture, and early layer feature reuse. Building upon the exploration of distinctive characteristics, we further employed the widely recognized method to identify models for dental age prediction. Consequently, we discovered two sets of models: one exhibiting superior performance, and the other being lightweight. The proposed approaches, namely AGENet and AGE-SPOS, demonstrated remarkable superiority and effectiveness in our experimental results. The proposed models, AGENet and AGE-SPOS, showed exceptional effectiveness in our experiments. AGENet outperformed other CNN models significantly by achieving outstanding results. Compared to Inception-v4, with the mean absolute error (MAE) of 1.70 and 20.46 B FLOPs, our AGENet reduced the FLOPs by 2.7×. The lightweight model, AGE-SPOS, achieved an MAE of 1.80 years with only 0.95 B FLOPs, surpassing MobileNetV2 by 0.18 years while utilizing fewer computational operations. In summary, we employed an effective DNN searching method for forensic age estimation, and our methodology and findings hold significant implications for age estimation with oral imaging.

Retraction Note: A versatile approach for dental age estimation using fuzzy neural network with teaching learning—based optimization classification

Retraction Note: A versatile approach for dental age estimation using fuzzy neural network with teaching learning—based optimization classification

Computed tomographic age estimation from the iliac crest and ischial tuberosity in an Indian population using supervised machine learning approaches.

Within the pelvis the iliac crest and ischial tuberosity display delayed ossification and fusion, thus, presenting as reliable maturity indicators. Amongst the different iliac crest and ischial tuberosity age estimation methods, the modified Kreitner-Kellinghaus stages constitute one of the more promising methods. The present study was directed towards establishing the applicability of the modified Kreitner-Kellinghaus method using five supervised machine learning approaches. Clinical CT scans of consenting individuals were collected and scored using the modified Kreitner-Kellinghaus method for the iliac crest and ischial tuberosity, independently. Age was subsequently estimated using different machine learning models. Cumulative scores computed from both markers were additionally employed for age estimation using machine learning. For iliac crest age estimation, Random Forest and Gradient Boosting Regression furnished lowest mean absolute error (2.42 years) and root mean square error (3.06 years). For ischial tuberosity age estimation, Gradient Boosting Regression garnered the lowest computations of mean absolute error (2.60 years) and root mean square error (3.09 years). For cumulative score based age estimation, Support Vector Regression and Gradient Boosting Regression yielded lowest mean absolute error (2.48 years) and root mean square error (3.07 years). Obtained error computations indicate that the iliac crest is a more accurate age marker in comparison to the ischial tuberosity. Additionally, cumulative score-based approaches garnered similar/ marginally more precise results in comparison to the iliac crest with all five models. This marginal improvement is not sufficient to justify employing the relatively more complicated cumulative score-based approach for age estimation. Hence, whenever available, the iliac crest should be preferred over the ischial tuberosity/ cumulative score-based approaches for age estimation.

Evaluating the impact of hot water killing larvae on gene expression using the transformer gene in Cochliomyia macellaria (Diptera: Calliphoridae).

In forensic entomology, determining the age of a larva from a body to estimate time since death is commonly performed through the measurement of a physical trait. Gene expression has been studied as an alternative age estimation approach, but the storage conditions required for these studies are different than those used in forensic entomological casework. Studies analyzing gene expression prioritize the preservation of RNA, which requires fresh tissue and ultra-cold storage. Casework, in contrast, utilizes hot water killing specimens that may not be analyzed for a long period after collection. In the current study, the impact of hot water killing on gene expression was assessed for larval samples of the forensically important blow fly, Cochliomyia macellaria. Successful amplification of the sex-determining gene, transformer, was tested across larvae ranging in size from 3.22 to 16.85 mm in length after storage times of 1-2 weeks, 4-5 weeks, and 8-9 weeks at 4°C in RNAlater. Larvae hot water killed were processed in tandem with larvae stored live to allow for a direct assessment of the impact of boiling on gene expression. As expected, the transformer gene was successfully amplified in all larvae stored live. For the hot water-killed larvae, the success rate was only slightly lower, with 3 out of 75 larvae not generating a sex-specific band pattern. The results show gene expression can be used for hot water-killed samples, though future work across different genes, species, and extending to quantitative gene expression methods is needed.

Artificial Intelligence as a Decision-Making Tool in Forensic Dentistry: A Pilot Study with I3M.

Expert determination of the third molar maturity index (I3M) constitutes one of the most common approaches for dental age estimation. This work aimed to investigate the technical feasibility of creating a decision-making tool based on I3M to support expert decision-making. Methods: The dataset consisted of 456 images from France and Uganda. Two deep learning approaches (Mask R-CNN, U-Net) were compared on mandibular radiographs, leading to a two-part instance segmentation (apical and coronal). Then, two topological data analysis approaches were compared on the inferred mask: one with a deep learning component (TDA-DL), one without (TDA). Regarding mask inference, U-Net had a better accuracy (mean intersection over union metric (mIoU)), 91.2% compared to 83.8% for Mask R-CNN. The combination of U-Net with TDA or TDA-DL to compute the I3M score revealed satisfying results in comparison with a dental forensic expert. The mean ± SD absolute error was 0.04 ± 0.03 for TDA, and 0.06 ± 0.04 for TDA-DL. The Pearson correlation coefficient of the I3M scores between the expert and a U-Net model was 0.93 when combined with TDA and 0.89 with TDA-DL. This pilot study illustrates the potential feasibility to automate an I3M solution combining a deep learning and a topological approach, with 95% accuracy in comparison with an expert.

New Coarse-to-Fine Approaches for Age Estimation Based on Separable Convolutions

New Coarse-to-Fine Approaches for Age Estimation Based on Separable Convolutions

Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors.

The difference between the chronological and biological brain age, called the brain age gap (BAG), has been identified as a promising biomarker to detect deviation from normal brain aging and to indicate the presence of neurodegenerative diseases. Moreover, the BAG has been shown to encode biological information about general health, which can be measured through cardiovascular risk factors. Current approaches for biological brain age estimation, and therefore BAG estimation, either depend on hand-crafted, morphological measurements extracted from brain magnetic resonance imaging (MRI) or on direct analysis of brain MRI images. The former can be processed with traditional machine learning models while the latter is commonly processed with convolutional neural networks (CNNs). Using a multimodal setting, this study aims to compare both approaches in terms of biological brain age prediction accuracy and biological information captured in the BAG. T1-weighted MRI, containing brain tissue information, and magnetic resonance angiography (MRA), providing information about brain arteries, from 1,658 predominantly healthy adults were used. The volumes, surface areas, and cortical thickness of brain structures were extracted from the T1-weighted MRI data, while artery density and thickness within the major blood flow territories and thickness of the major arteries were extracted from MRA data. Independent multilayer perceptron and CNN models were trained to estimate the brain age from the hand-crafted features and image data, respectively. Next, both approaches were fused to assess the benefits of combining image data and hand-crafted features for brain age prediction. The combined model achieved a mean absolute error of 4 years between the chronological and predicted biological brain age. Among the independent models, the lowest mean absolute error was observed for the CNN using T1-weighted MRI data (4.2 years). When evaluating the BAGs obtained using the different approaches and imaging modalities, diverging associations between cardiovascular risk factors were found. For example, BAGs obtained from the CNN models showed an association with systolic blood pressure, while BAGs obtained from hand-crafted measurements showed greater associations with obesity markers. In conclusion, the use of more diverse sources of data can improve brain age estimation modeling and capture more diverse biological deviations from normal aging.

Tooth Wear Age Estimation of Ruminants from Archaeological Sites

The teeth of ruminants (cud-chewing herbivores) can be used to estimate age. Tooth wear age estimation is an especially valuable method in archaeological research because it is non-destructive, efficient, and is adaptable to multiple species, which provides effective results. The objective of this paper is to review tooth wear age estimation approaches taken with a focus on cervid (deer) and caprine (sheep and goat) mandibles. I discuss the process of dental attrition involving ruminant chewing, digestion, and feeding behaviour, as well as factors that affect the rate of wear including individual and population variance. The approaches to tooth wear age estimation have been divided into three overarching categories: the Crown Height Method, the Visual Wear Pattern Method, and the Wear Trait Scoring Method. These approaches are all non-destructive and require similar assumptions about the regularities of tooth wear. Each involves different levels of accuracy, ease of use, efficiency, and applicability to archaeological mandibles. This paper highlights the strengths and weaknesses for these approaches and explains that these various methods reviewed are each better suited to different research situations. Taken together, tooth wear age estimation is a valuable tool that zooarchaeologists employ to reconstruct age-based demographic profiles of animal remains recovered from archaeological sites, illustrating how people interacted with and used them.

Quantitative and Qualitative Approaches for Dental Age Assessment in Sub Adult Portuguese Population: European Regression Formula and Demirjian Stages.

Over the last decade, the criminal justice system among the European countries deals with a new group of people, the undocumented people. In the criminal field, most of the suspected claimed to be under the minor legal age according to the type of crime. The government of each European country is committed to protecting the children and vulnerable people. There are serious safety concerns since adults falsely claim to be minors and legal systems oppose to such claims. In this study, we have used different age assessment methods and some of them without accuracy. To meet the general considerations we developed a study using the current dental age assessment methods. Hence, for the Portuguese population, this study aims to validate the European regression formula between the ages of 6 and 15 years of age; to estimate the cut-off point for the age of 12 years, and to compare the quantitative approach of European regression formula with Demirjian's qualitative approach for age estimation. 483 orthopantomograms were analysed using the European formula and Demirjian scoring stages. A new method, Model 2, was designed to better suit the Portuguese population. The European formula: average age underestimation of 4.88 for the third quadrant and 4.04 months for the fourth quadrant; and a mean absolute error (MAE) of 10.93 and 10.68 months respectively. Demirjian method: average overestimation of 8.70 months, MAE of 12.85 months. In Model 2: MAE of 9.37 months for the third quadrant, and 9.28 months for the fourth quadrant. Both European formula and Demirjian method had an area under ROC curves results above 0.93. Discrepancy of sensitivity between methods for the specific cut-off point: 11.48. The results obtained in this study can be extrapolated to 87.33% of the Portuguese population. The European regression formula can be applied for the Portuguese population, and seems to be more accurate than Demirjian's methodology in this population. Nevertheless, both European regression formula and Demirjian method present similarly suitable results in the classification of 12 years of age, although there is a noticeable discrepancy favouring the European regression formula. The European Formula when applied for criminal age assessment, independently of population, is mandatory to indicate to the judicial institutions, the sensitivity of the results.

Robust and Lightweight System for Gait-based Age Estimation towards Viewing Angle Variations

Abstract: Gait based age recognition is a very challenging task, as it involves multiple hurdles such as change in viewpoint of the person. The proposed system handles this problem by performing a sequence of tasks such as GEI formation from silhouette, applying DCT on GEI and extracting the features and finally using MLP for age estimation. The proposed system proves its effectiveness comparing the performance with state of art methods -conventional methods and deep learning based methods. The performance of the system is estimated on OU-MVLP and OULP-Age datasets. The experimental results show the robustness of the system against viewing angle variations. Background: In computer vision applications, gait-based age estimation across several cameras is critical, especially when following the same person in various viewpoints. Objective: To implement the system which adopts lightweight approach for gait-based age estimation. Method: The proposed system uses a combination of the discrete cosine transform (DCT) and multi-layer perceptron (MLP) on gait energy image (GEI) to perform age estimation. Result: The performance of the system is extensively evaluated on the OU-MVLP and OULP-Age datasets. Conclusion: The proposed system attains best mean absolute error (MAE) of 5.05 (in years) for the OU-MVLP dataset, and 5.65 for the OULP dataset.

Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging.

Many machine learning tasks in neuroimaging aim at modeling complex relationships between a brain's morphology as seen in structural MR images and clinical scores and variables of interest. A frequently modeled process is healthy brain aging for which many image-based brain age estimation or age-conditioned brain morphology template generation approaches exist. While age estimation is a regression task, template generation is related to generative modeling. Both tasks can be seen as inverse directions of the same relationship between brain morphology and age. However, this view is rarely exploited and most existing approaches train separate models for each direction. In this paper, we propose a novel bidirectional approach that unifies score regression and generative morphology modeling and we use it to build a bidirectional brain aging model. We achieve this by defining an invertible normalizing flow architecture that learns a probability distribution of 3D brain morphology conditioned on age. The use of full 3D brain data is achieved by deriving a manifold-constrained formulation that models morphology variations within a low-dimensional subspace of diffeomorphic transformations. This modeling idea is evaluated on a database of MR scans of more than 5000 subjects. The evaluation results show that our bidirectional brain aging model (1) accurately estimates brain age, (2) is able to visually explain its decisions through attribution maps and counterfactuals, (3) generates realistic age-specific brain morphology templates, (4) supports the analysis of morphological variations, and (5) can be utilized for subject-specific brain aging simulation.

Deep focus approach for accurate bone age estimation from lateral cephalogram

Background/purposeBone age is a useful indicator of children's growth and development. Recently, the rapid development of deep-learning technique has shown promising results in estimating bone age. This study aimed to devise a deep-learning approach for accurate bone-age estimation by focusing on the cervical vertebrae on lateral cephalograms of growing children using image segmentation. Materials and methodsWe included 900 participants, aged 4–18 years, who underwent lateral cephalogram and hand-wrist radiograph on the same day. First, cervical vertebrae segmentation was performed from the lateral cephalogram using DeepLabv3+ architecture. Second, after extracting the region of interest from the segmented image for preprocessing, bone age was estimated through transfer learning using a regression model based on Inception-ResNet-v2 architecture. The dataset was divided into train:test sets in a ratio of 4:1; five-fold cross-validation was performed at each step. ResultsThe segmentation model possessed average accuracy, intersection over union, and mean boundary F1 scores of 0.956, 0.913, and 0.895, respectively, for the segmentation of cervical vertebrae from lateral cephalogram. The regression model for estimating bone age from segmented cervical vertebrae images yielded average mean absolute error and root mean squared error values of 0.300 and 0.390 years, respectively. The coefficient of determination of the proposed method for the actual and estimated bone age was 0.983. Our method visualized important regions on cervical vertebral images to make a prediction using the gradient-weighted regression activation map technique. ConclusionResults showed that our proposed method can estimate bone age by lateral cephalogram with sufficiently high accuracy.

Children and Adolescent Dental Age Estimation by the Willems and Al Qahtani Methods in Surabaya, Indonesia.

Objectives Dental age estimation has been employed in a range of legal operations as well as catastrophe victim identification. Dental age estimation is regarded as an appropriate method for estimating a person's age because there is a high association between age and teeth. This study aims to assess the suitability of the Al Qahtani and Willems dental age estimation approaches for the Indonesian children and adolescent population. Methods A total of 150 panoramic radiographs of patients (75 boys and 75 girls, 6-17 years old) were obtained from the Department of Radiology, Airlangga University, Indonesia. One researcher analyzed estimated dental age (EDA) twice in a one-week time-lapse using the Willems and Al Qahtani methods. The statistical analysis of the present study was carried out using IBM® SPSS® Statistics version 25.0 (IBM, Armonk, NY, USA). Results The mean of this study's chronological age (CA) was 11.60 ± 3.41. Using the Willems method, the mean difference between CA and EDA for boys and girls was -0.41 ± 0.90. The mean difference between CA and EDA for boys and girls is 0.33 ± 0.61 using the Al Qahtani method. Conclusions According to the findings of this investigation, the dental age estimation method proposed by Al Qahtani and Willems can be applied to the population in Surabaya. However, a comprehensive study is required when using this method because the data revealed significant statistical disparities between the two methods.

A Hierarchical Approach toward Prediction of Human Biological Age from Masked Facial Image Leveraging Deep Learning Techniques

The lifestyle of humans has changed noticeably since the contagious COVID-19 disease struck globally. People should wear a face mask as a protective measure to curb the spread of the contagious disease. Consequently, real-world applications (i.e., electronic customer relationship management) dealing with human ages extracted from face images must migrate to a robust system proficient to estimate the age of a person wearing a face mask. In this paper, we proposed a hierarchical age estimation model from masked facial images in a group-to-specific manner rather than a single regression model because age progression across different age groups is quite dissimilar. Our intention was to squeeze the feature space among limited age classes so that the model could fairly discern age. We generated a synthetic masked face image dataset over the IMDB-WIKI face image dataset to train and validate our proposed model due to the absence of a benchmark masked face image dataset with real age annotations. We somewhat mitigated the data sparsity problem of the large public IMDB-WIKI dataset using off-the-shelf down-sampling and up-sampling techniques as required. The age estimation task was fully modeled like a deep classification problem, and expected ages were formulated from SoftMax probabilities. We performed a classification task by deploying multiple low-memory and higher-accuracy-based convolutional neural networks (CNNs). Our proposed hierarchical framework demonstrated marginal improvement in terms of mean absolute error (MAE) compared to the one-off model approach for masked face real age estimation. Moreover, this research is perhaps the maiden attempt to estimate the real age of a person from his/her masked face image.

Reliability of aspartic acid racemization rate for chronological age estimation-a systematic review and meta-analysis.

The biochemical approach of dental age using aspartic acid racemization has been widely reported. The aim of this systematic review was to determine the reliability and accuracy of dental age estimation using aspartic acid racemization rate analysis. Eight research databases (PubMed, Google Scholar, Embase, Cochrane, MEDLINE, TRIP, Web of Science, and Scopus) were utilised to gather and assess published literatures in compliance with the PRISMA 2020 guidelines and reported in PROSPERO (CRD42020208877). This systematic review and meta-analysis focused solely on cross-sectional studies. The quality evaluation was performed using the GRADE system. The standardized mean difference between estimated and chronological age was meta-analyzed using the random effects model. The literature review yielded 213 studies, of which 26 were considered acceptable for inclusion in this report. Out of 26 studies, 19 presented sufficient evidence for meta-analysis and the remaining 7 were used to construct a qualitative review. According to the meta-analysis, premolar-related studies had the least variability (Tau2 = 0.23; I2 = 72%) among other tooth groups. A substantial degree of heterogeneity was found in every type of tooth. Only the premolar teeth had lower degree variability; thus, it is safe to believe that the premolar tooth is the best for this type of age estimation. It is recommended to develop population-specific mathematical equations to improve the accuracy of this age estimation approach.

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

AbstractAiming 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.

Cross-Racial Automatic Age Estimation from Facial Images using Deep Learning

This paper presents a deep learning approach for age estimation of human beings using their facial images. The different racial groups based on skin colour have been incorporated in the annotations of the images in the dataset, while ensuring an adequate distribution of subjects across the racial groups so as to achieve an accurate Automatic Facial Age Estimation (AFAE). The principle of transfer learning is applied to the ResNet50 Convolutional Neural Network (CNN) initially pretrained for the task of object classification and finetuning it’s hyperparameters to propose an AFAE system that can be used to automate ages of humans across multiple racial groups. The mean absolute error of 4.25 years is obtained at the end of the research which proved the effectiveness and superiority of the proposed method.