Optimizing Age Estimation in Facial Images with Advanced Multi-Class Classification Techniques
Automatic age and gender prediction from facial images is increasingly crucial for applications in security, marketing, and social media. Existing systems often face challenges related to accuracy, demographic generalization, and bias. This study addresses these issues by developing a deep learni...
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INTI International University
2024
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| Online Access: | http://eprints.intimal.edu.my/1982/1/521 http://eprints.intimal.edu.my/1982/2/joit2024_08b.pdf http://eprints.intimal.edu.my/1982/ http://ipublishing.intimal.edu.my/joint.html |
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| _version_ | 1834879008769048576 |
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| author | R., Karthickmanoj S.Aasha, Nandhini D., Lakshmi R., Rajasree |
| author_facet | R., Karthickmanoj S.Aasha, Nandhini D., Lakshmi R., Rajasree |
| author_sort | R., Karthickmanoj |
| building | INTI Library |
| collection | Institutional Repository |
| content_provider | INTI International University |
| content_source | INTI Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Automatic age and gender prediction from facial images is increasingly crucial for applications in
security, marketing, and social media. Existing systems often face challenges related to accuracy,
demographic generalization, and bias. This study addresses these issues by developing a deep
learning-based system utilizing Convolutional Neural Networks (CNNs) for enhanced
classification of age and gender. The key research gaps include limited accuracy, insufficient
handling of diverse data, and model bias. The proposed approach encompasses data acquisition,
preprocessing, and the design of a CNN architecture within a multi-class classification framework.
Various CNN models are evaluated, incorporating transfer learning, hyperparameter optimization,
and regularization techniques to improve performance. The system's effectiveness is assessed
through metrics such as classification accuracy, precision, recall, and robustness across different
demographic groups. Results indicate significant advancements in prediction accuracy and model
generalization compared to existing methods. The technology holds practical applications in
security, personalized marketing, and social networking. Challenges such as model bias and the
need for diverse datasets are addressed, with future research aimed at further refining the model
and expanding its applicability. This work highlights the substantial improvements deep learning
offers to facial recognition technologies. |
| format | Article |
| id | my-inti-eprints.1982 |
| institution | INTI International University |
| language | en en |
| publishDate | 2024 |
| publisher | INTI International University |
| record_format | eprints |
| spelling | my-inti-eprints.19822025-06-13T03:15:21Z http://eprints.intimal.edu.my/1982/ Optimizing Age Estimation in Facial Images with Advanced Multi-Class Classification Techniques R., Karthickmanoj S.Aasha, Nandhini D., Lakshmi R., Rajasree Q Science (General) QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software Automatic age and gender prediction from facial images is increasingly crucial for applications in security, marketing, and social media. Existing systems often face challenges related to accuracy, demographic generalization, and bias. This study addresses these issues by developing a deep learning-based system utilizing Convolutional Neural Networks (CNNs) for enhanced classification of age and gender. The key research gaps include limited accuracy, insufficient handling of diverse data, and model bias. The proposed approach encompasses data acquisition, preprocessing, and the design of a CNN architecture within a multi-class classification framework. Various CNN models are evaluated, incorporating transfer learning, hyperparameter optimization, and regularization techniques to improve performance. The system's effectiveness is assessed through metrics such as classification accuracy, precision, recall, and robustness across different demographic groups. Results indicate significant advancements in prediction accuracy and model generalization compared to existing methods. The technology holds practical applications in security, personalized marketing, and social networking. Challenges such as model bias and the need for diverse datasets are addressed, with future research aimed at further refining the model and expanding its applicability. This work highlights the substantial improvements deep learning offers to facial recognition technologies. INTI International University 2024-08 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1982/1/521 text en cc_by_4 http://eprints.intimal.edu.my/1982/2/joit2024_08b.pdf R., Karthickmanoj and S.Aasha, Nandhini and D., Lakshmi and R., Rajasree (2024) Optimizing Age Estimation in Facial Images with Advanced Multi-Class Classification Techniques. Journal of Innovation and Technology, 2024 (08). pp. 1-7. ISSN 2805-5179 http://ipublishing.intimal.edu.my/joint.html |
| spellingShingle | Q Science (General) QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software R., Karthickmanoj S.Aasha, Nandhini D., Lakshmi R., Rajasree Optimizing Age Estimation in Facial Images with Advanced Multi-Class Classification Techniques |
| title | Optimizing Age Estimation in Facial Images with Advanced Multi-Class
Classification Techniques |
| title_full | Optimizing Age Estimation in Facial Images with Advanced Multi-Class
Classification Techniques |
| title_fullStr | Optimizing Age Estimation in Facial Images with Advanced Multi-Class
Classification Techniques |
| title_full_unstemmed | Optimizing Age Estimation in Facial Images with Advanced Multi-Class
Classification Techniques |
| title_short | Optimizing Age Estimation in Facial Images with Advanced Multi-Class
Classification Techniques |
| title_sort | optimizing age estimation in facial images with advanced multi-class
classification techniques |
| topic | Q Science (General) QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.intimal.edu.my/1982/1/521 http://eprints.intimal.edu.my/1982/2/joit2024_08b.pdf http://eprints.intimal.edu.my/1982/ http://ipublishing.intimal.edu.my/joint.html |
| url_provider | http://eprints.intimal.edu.my |