Features and Methods of Human Age Estimation: Opportunities and Challenges in Medical Image Processing

Patil, Kirti A.

doi:10.17762/turcomat.v12i1s.1770

Cited by 3 publications

References 80 publications

(89 reference statements)

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An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

Neggaz

Fizazi

2022

Soft Comput

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Human facial analysis (HFA) has recently become an attractive topic for computer vision research due to technological progress and mobile applications. HFA explores several issues as gender recognition (GR), facial expression, age, and race recognition for automatically understanding social life. This study explores HFA from the angle of recognizing a person’s gender from their face. Several hard challenges are provoked, such as illumination, occlusion, facial emotions, quality, and angle of capture by cameras, making gender recognition more difficult for machines. The Archimedes optimization algorithm (AOA) was recently designed as a metaheuristic-based population optimization method, inspired by the Archimedes theory’s physical notion. Compared to other swarm algorithms in the realm of optimization, this method promotes a good balance between exploration and exploitation. The convergence area is increased By incorporating extra data into the solution, such as volume and density. Because of the preceding benefits of AOA and the fact that it has not been used to choose the best area of the face, we propose utilizing a wrapper feature selection technique, which is a real motivation in the field of computer vision and machine learning. The paper’s primary purpose is to automatically determine the optimal face area using AOA to recognize the gender of a human person categorized by two classes (Men and women). In this paper, the facial image is divided into several subregions (blocks), where each area provides a vector of characteristics using one method from handcrafted techniques as the local binary pattern (LBP), histogram-oriented gradient (HOG), or gray-level co-occurrence matrix (GLCM). Two experiments assess the proposed method (AOA): The first employs two benchmarking datasets: the Georgia Tech Face dataset (GT) and the Brazilian FEI dataset. The second experiment represents a more challenging large dataset that uses Gallagher’s uncontrolled dataset. The experimental results show the good performance of AOA compared to other recent and competitive optimizers for all datasets. In terms of accuracy, the AOA-based LBP outperforms the state-of-the-art deep convolutional neural network (CNN) with 96.08% for the Gallagher’s dataset.

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An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

Neggaz

Fizazi

2022

Soft Comput

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Advancements in Dental Age Estimation: A Comprehensive Review of Convolutional Neural Networks and Ethical Considerations

Domilin Shyni,

Sheeja Herobin Rani,

Rexilin Sheeba

2024

2024 10th International Conference on Communication and Signal Processing (ICCSP)

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Dental Age Estimation Using Deep Learning: A Comparative Survey

et al. 2023

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The significance of age estimation arises from its applications in various fields, such as forensics, criminal investigation, and illegal immigration. Due to the increased importance of age estimation, this area of study requires more investigation and development. Several methods for age estimation using biometrics traits, such as the face, teeth, bones, and voice. Among then, teeth are quite convenient since they are resistant and durable and are subject to several changes from childhood to birth that can be used to derive age. In this paper, we summarize the common biometrics traits for age estimation and how this information has been used in previous research studies for age estimation. We have paid special attention to traditional machine learning methods and deep learning approaches used for dental age estimation. Thus, we summarized the advances in convolutional neural network (CNN) models to estimate dental age from radiological images, such as 3D cone-beam computed tomography (CBCT), X-ray, and orthopantomography (OPG) to estimate dental age. Finally, we also point out the main innovations that would potentially increase the performance of age estimation systems.

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Features and Methods of Human Age Estimation: Opportunities and Challenges in Medical Image Processing

Cited by 3 publications

References 80 publications

An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

Advancements in Dental Age Estimation: A Comprehensive Review of Convolutional Neural Networks and Ethical Considerations

Dental Age Estimation Using Deep Learning: A Comparative Survey

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