Joint Age Estimation and Gender Classification of Asian Faces Using Wide ResNet

Huynh,; Nguyễn, Hoàng Long

doi:10.1007/s42979-020-00294-w

Cited by 16 publications

(12 citation statements)

References 15 publications

(25 reference statements)

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“…Thus, wider residual networks were established once this was determined. However, inserting a dropout between the convolutional layers (as opposed to within the residual block) made the learning more effective in WideResNet [ 121 , 122 ].…”

Section: Cnn Architecturesmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

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Section: Cnn Architecturesmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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“…Given the forensic data, it is important to estimate age and sex. Recently, by applying the wide ResNet model to facial images, age and gender have been excellently distinguished 24 . In the traditional nonautomatic method, the age estimation formula or trend has been obtained separately because males and females have different progression of tooth and bone development and aging processes 11,25,26 .…”

Section: Gender Differences In the Auc Values Of Linear And Nonlinear...mentioning

confidence: 99%

Age Group Prediction with Panoramic Radiomorphometric Parameters Using Machine Learning Algorithms

Lee

Won

Auh

et al. 2022

Preprint

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Aim: The aim of this study is to investigate the relationship of 18 radiomorphological parameters of panoramic radiographs based on age, and to estimate the age group of people with permanent dentition in a non-invasive, comprehensive, and accurate manner using five machine learning algorithms.Methods: For the study population (209 men and 262 women; mean age, 32.12±18.71 years), 471 digital panoramic radiographs of Korean individuals were applied. The participants were divided into three groups (with a 20-year age gap) and six groups (with a 10-year age gap), and each age group was estimated using the following five machine learning models: a linear discriminant analysis (LDA), logistic regression, kernelized support vector machines, multilayer perceptron, and extreme gradient boosting. Finally, a Fisher discriminant analysis was used to visualize the data configuration. Results: In the prediction of the three age-group classification, the areas under the curve (AUCs) obtained for classifying young ages (10–19 y) ranged from 0.85 to 0.88 for five different machine learning models. The AUC values of the older age group (50–69 y) ranged from 0.82 to 0.88, and those of adults (20–49 y) were approximately 0.73. In the six age-group classification, the best scores were also found in age groups 1 (10–19 y) and 6 (60–69 y), with mean AUCs ranging from 0.85 to 0.87 and 80 to 0.90, respectively. A feature analysis based on LDA weights showed that the L-Pulp Area was important for discriminating young ages (10–49 y), and L-Crown, U-Crown, L-Implant, U-Implant, and Periodontitis were used as predictors for discriminating older ages (50–69 y). Conclusion: We established acceptable linear and nonlinear machine learning models for a dental age group estimation using multiple maxillary and mandibular radiomorphometric parameters.

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“…The UTKFace database was used for testing the performance of the system. Huynh and Nguyen, [13] proposed a system based on a Wide ResNet CNN for age and gender classification of certain Asian descents. Their approach involves an image augmentation stage which was done by using Random erasing and Mixup processing in order to improve the quality of the Megaage_Asian dataset and a training state, where the CNN was trained for age classification.…”

Section: Related Workmentioning

confidence: 99%

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

2021

IJETER

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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.

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Joint Age Estimation and Gender Classification of Asian Faces Using Wide ResNet

Cited by 16 publications

References 15 publications

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Age Group Prediction with Panoramic Radiomorphometric Parameters Using Machine Learning Algorithms

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

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