Research on Age Estimation Algorithm Based on Structured Sparsity

2019 ◽  
Vol 33 (06) ◽  
pp. 1956006 ◽  
Author(s):  
Zijiang Zhu ◽  
Junshan Li ◽  
Yi Hu ◽  
Xiaoguang Deng
Keyword(s):  
Age Estimation ◽  
Age Distribution ◽  
Estimation Algorithm ◽  
Absolute Error ◽  
Sparse Learning ◽  
Estimation Model ◽  
Accumulation Index ◽  
Error Accumulation ◽  
Index Curve ◽  
Facial Images

In order to solve the inaccuracy of age estimation dataset and the imbalance of age distribution, this paper proposes an age estimation model based on the structured sparse learning. Firstly, the Multi-label representation of facial images is performed by age, and the age estimation model is trained by solving the model matrix. Finally, the correlation with all age labels is calculated according to the facial images and age estimation model to be tested, and the most correlated age is taken as the predicted age. This paper sets up a series of verification experiments, and analyzes the structured sparse age estimation model from several perspectives. The proposed algorithm has achieved good results in the evaluation of indexes such as the mean absolute error, accumulation index curve and convergence rate, and has designed the demo system to put the model into use. Facts prove that the age estimation model proposed in this paper may achieve a good estimation effect.

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

2021 ◽  
Vol 9 (9) ◽  
pp. 1288-1294
Keyword(s):  
Deep Learning ◽  
Age Estimation ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Learning Approach ◽  
Racial Groups ◽  
Human Beings ◽  
The Mean ◽  
Facial Images ◽  
Facial Age

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.

scholarly journals Validating dental age estimation in Kenyan black children and adolescents using the Willems method

2020 ◽  
pp. 002580242097737
Author(s):  
Maria Cadenas de Llano-Pérula ◽  
Eunice Kihara ◽  
Patrick Thevissen ◽  
Donna Nyamunga ◽  
Steffen Fieuws ◽  
...  
Keyword(s):  
Age Estimation ◽  
Tooth Development ◽  
Absolute Error ◽  
Black Children ◽  
Specific Model ◽  
Permanent Teeth ◽  
Estimation Model ◽  
One Year ◽  
Willems Method ◽  
Age Prediction

Purpose This study aimed to validate the Willems Belgian Caucasian (Willems BC) age estimation model in a Kenyan sample, to develop and validate a Kenyan-specific (Willems KB) age estimation model and to compare the age prediction performances of both models. Methods Panoramic radiographs of 1038 (523 female, 515 male) Kenyan children without missing permanent teeth and without all permanent teeth fully developed (except third molars) were retrospectively selected. Tooth development of the seven lower-left permanent teeth was staged according to Demirjian et al. The Willems BC model, performed on a Belgian Caucasian sample and a constructed Kenyan-specific model (Willems KB) were validated on the Kenyan sample. Their age prediction performances were quantified and compared using the mean error (ME), mean absolute error (MAE) and root-mean-square error (RMSE). Results The ME with Willems BC method equalled zero. Hence, there was no systematic under- or overestimation of the age. For males and females separately, the ME with Willems BC was significantly different from zero, but negligible in magnitude (–0.04 and 0.04, respectively). Willems KB was found not to outperform Willems BC, since the MAE and RMSE were comparable (0.98 vs 0.97 and 1.31 vs 1.29, respectively). Although Willems BC resulted in a higher percentage of subjects with predicted age within a one-year difference of the true age (63.3% vs 60.4%, p=0.018), this cannot be considered as clinically relevant. Conclusion There is no reason to use a country-specific (Willems KB) model in children from Kenya instead of the original Willems (BC) model.

scholarly journals Age Estimation and its Progression from Face Images

2019 ◽  
Vol 8 (3) ◽  
pp. 2351-2355
Keyword(s):  
Age Estimation ◽  
Performance Metrics ◽  
Research Work ◽  
Human Aging ◽  
Estimation Model ◽  
Face Model ◽  
Face Images ◽  
Real World Applications ◽  
Facial Images ◽  
Great Knowledge

Face model improves the performance of evaluating the accurate age estimation with facial images and has enormous real-world applications. Human aging is a process of growing gradually old and mature. However, it is slow, depends upon person to person and most important it is irreversible. This paper mainly focuses on the various face model techniques, their performance metrics, databases, age estimation challenges to provide the researcher a great knowledge with recent journals in this field. Age estimation process progress with two modules: first part is feature extraction from the image and second module is age estimation. The accuracy or the desired output from age estimation model largely depends upon the features extraction, which if selected appropriately helps to achieve better results for research work.

Estimation of Hemi-Abdominal-Based Free Flap Weight Using Two Computed Tomography-Derived Measurements

2021 ◽  
Author(s):  
Nicolas Greige ◽  
Bryce Liu ◽  
David Nash ◽  
Katie E. Weichman ◽  
Joseph A. Ricci
Keyword(s):  
Computed Tomography ◽  
Breast Reconstruction ◽  
Free Flap ◽  
Subcutaneous Tissue ◽  
Free Tissue Transfer ◽  
Absolute Error ◽  
Estimation Methods ◽  
Weight Estimation ◽  
Estimation Model ◽  
Split Sample

Abstract Background Accurate flap weight estimation is crucial for preoperative planning in microsurgical breast reconstruction; however, current flap weight estimation methods are time consuming. It was our objective to develop a parsimonious and accurate formula for the estimation of abdominal-based free flap weight. Methods Patients who underwent hemi-abdominal-based free tissue transfer for breast reconstruction at a single institution were retrospectively reviewed. Subcutaneous tissue thicknesses were measured on axial computed tomography angiograms at several predetermined points. Multivariable linear regression was used to generate the parsimonious flap weight estimation model. Split-sample validation was used to for internal validation. Results A total of 132 patients (196 flaps) were analyzed, with a mean body mass index of 31.2 ± 4.0 kg/m2 (range: 22.6–40.7). The mean intraoperative flap weight was 990 ± 344 g (range: 368–2,808). The full predictive model (R 2 = 0.68) estimated flap weight using the Eq. 91.3x + 36.4y + 6.2z – 1030.0, where x is subcutaneous tissue thickness (cm) 5 cm lateral to midline at the level of the anterior superior iliac spine (ASIS), y is distance (cm) between the skin overlying each ASIS, and z is patient weight (kg). Two-thirds split-sample validation was performed using 131 flaps to build a model and the remaining 65 flaps for validation. Upon validation, we observed a median percent error of 10.2% (interquartile range [IQR]: 4.5–18.5) and a median absolute error of 108.6 g (IQR: 45.9–170.7). Conclusion We developed and internally validated a simple and accurate formula for the preoperative estimation of hemi-abdominal-based free flap weight for breast reconstruction.

Towards Accuracy Enhancement of Age Group Classification Using Generative Adversarial Networks

2021 ◽  
pp. 1-17
Author(s):  
Khaled ELKarazle ◽  
Valliappan Raman ◽  
Patrick Then
Keyword(s):  
Age Estimation ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Original Dataset ◽  
Age Group Classification ◽  
Facial Images

Age estimation models can be employed in many applications, including soft biometrics, content access control, targeted advertising, and many more. However, as some facial images are taken in unrestrained conditions, the quality relegates, which results in the loss of several essential ageing features. This study investigates how introducing a new layer of data processing based on a super-resolution generative adversarial network (SRGAN) model can influence the accuracy of age estimation by enhancing the quality of both the training and testing samples. Additionally, we introduce a novel convolutional neural network (CNN) classifier to distinguish between several age classes. We train one of our classifiers on a reconstructed version of the original dataset and compare its performance with an identical classifier trained on the original version of the same dataset. Our findings reveal that the classifier which trains on the reconstructed dataset produces better classification accuracy, opening the door for more research into building data-centric machine learning systems.

Age estimation in facial images through transfer learning

2018 ◽  
Vol 30 (1) ◽  
pp. 177-187 ◽  
Author(s):  
F. Dornaika ◽  
I. Arganda-Carreras ◽  
C. Belver
Keyword(s):  
Transfer Learning ◽  
Age Estimation ◽  
Facial Images

Machine learning and Grad-Cam based vascular aging assessment using photoplethysmogram (Preprint)

2021 ◽  
Author(s):  
Hangsik Shin
Keyword(s):  
Machine Learning ◽  
Correlation Coefficient ◽  
Age Estimation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Vascular Aging ◽  
Squared Error ◽  
Vascular Age

BACKGROUND Arterial stiffness due to vascular aging is a major indicator for evaluating cardiovascular risk. OBJECTIVE In this study, we propose a method of estimating age by applying machine learning to photoplethysmogram for non-invasive vascular age assessment. METHODS The machine learning-based age estimation model that consists of three convolutional layers and two-layer fully connected layers, was developed using segmented photoplethysmogram by pulse from a total of 752 adults aged 19–87 years. The performance of the developed model was quantitatively evaluated using mean absolute error, root-mean-squared-error, Pearson’s correlation coefficient, coefficient of determination. The Grad-Cam was used to explain the contribution of photoplethysmogram waveform characteristic in vascular age estimation. RESULTS Mean absolute error of 8.03, root mean squared error of 9.96, 0.62 of correlation coefficient, and 0.38 of coefficient of determination were shown through 10-fold cross validation. Grad-Cam, used to determine the weight that the input signal contributes to the result, confirmed that the contribution to the age estimation of the photoplethysmogram segment was high around the systolic peak. CONCLUSIONS The machine learning-based vascular aging analysis method using the PPG waveform showed comparable or superior performance compared to previous studies without complex feature detection in evaluating vascular aging. CLINICALTRIAL 2015-0104

Real-Time Age Estimation from Facial Images Using YOLO and EfficientNet

2021 ◽  
pp. 275-284
Author(s):  
Giovanna Castellano ◽  
Berardina De Carolis ◽  
Nicola Marvulli ◽  
Mauro Sciancalepore ◽  
Gennaro Vessio
Keyword(s):  
Real Time ◽  
Age Estimation ◽  
Facial Images
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