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

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 fac...

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Main Authors:	Alkaabi S., Yussof S., Al-Mulla S.
Other Authors:	59070935100
Format:	Article
Published:	Tech Science Press 2024
Subjects:	age estimation convolutional neural network Dental forensics generative adversarial network pseudo-labelling Convolution Convolutional neural networks Deep learning Digital forensics Image enhancement Neural network models Age estimation Convolutional neural network Dental forensic Dental images Forensic odontology Human expert Labelings Neural network model Pseudo-labeling Synthetic images Generative adversarial networks
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spelling	my.uniten.dspace-347002024-10-14T11:21:51Z Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling Alkaabi S. Yussof S. Al-Mulla S. 59070935100 16023225600 36473139200 age estimation convolutional neural network Dental forensics generative adversarial network pseudo-labelling Convolution Convolutional neural networks Deep learning Digital forensics Image enhancement Neural network models Age estimation Convolutional neural network Dental forensic Dental images Forensic odontology Human expert Labelings Neural network model Pseudo-labeling Synthetic images Generative adversarial networks 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 pseudo-labelling technique to label the generated images with proper age and gender. We used the combination of real and generated images to train Dental Age 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. � 2023 Tech Science Press. All rights reserved. Final 2024-10-14T03:21:51Z 2024-10-14T03:21:51Z 2023 Article 10.32604/cmc.2023.029914 2-s2.0-85141892582 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141892582&doi=10.32604%2fcmc.2023.029914&partnerID=40&md5=3d6e4208eda7c0816b49d401c1b39264 https://irepository.uniten.edu.my/handle/123456789/34700 74 2 2499 2516 All Open Access Gold Open Access Tech Science Press Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	age estimation convolutional neural network Dental forensics generative adversarial network pseudo-labelling Convolution Convolutional neural networks Deep learning Digital forensics Image enhancement Neural network models Age estimation Convolutional neural network Dental forensic Dental images Forensic odontology Human expert Labelings Neural network model Pseudo-labeling Synthetic images Generative adversarial networks
spellingShingle	age estimation convolutional neural network Dental forensics generative adversarial network pseudo-labelling Convolution Convolutional neural networks Deep learning Digital forensics Image enhancement Neural network models Age estimation Convolutional neural network Dental forensic Dental images Forensic odontology Human expert Labelings Neural network model Pseudo-labeling Synthetic images Generative adversarial networks Alkaabi S. Yussof S. Al-Mulla S. Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling
description	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 pseudo-labelling technique to label the generated images with proper age and gender. We used the combination of real and generated images to train Dental Age 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. � 2023 Tech Science Press. All rights reserved.
author2	59070935100
author_facet	59070935100 Alkaabi S. Yussof S. Al-Mulla S.
format	Article
author	Alkaabi S. Yussof S. Al-Mulla S.
author_sort	Alkaabi S.
title	Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling
title_short	Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling
title_full	Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling
title_fullStr	Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling
title_full_unstemmed	Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling
title_sort	enhancing cnn for forensics age estimation using cgan and pseudo-labelling
publisher	Tech Science Press
publishDate	2024
_version_	1814061191417298944
score	13.222552

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

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