September 10, 2025Iraqi Journal of ScienceOpen Access

Age Stage Prediction Using Light Convolution Neural Network

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Authors

MSMais Saad Safoq NANoor D. Al-ShakarchyUniversity of Kerbala HAHiba J. Aleqabie

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Implication

The proposed model demonstrates high accuracy in predicting age stages using facial images, indicating improvements in prediction performance across varying conditions.

Key Points

The model achieved an accuracy of 98.86% on the UTKFace dataset, showcasing its effectiveness in age-stage prediction.
Facial feature extraction enhances prediction performance, accounting for factors like lighting and facial expression.
The light convolution neural network architecture enables efficient computation without compromising accuracy.
This approach can significantly impact domains such as forensic science and social media by providing reliable age-stage insights.

Cite This Study

Safoq et al. (2025) studied this question.

synapsesocial.com/papers/68c23e4fb210217d64791f3f https://doi.org/10.24996/ijs.2025.66.7.23

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Implication

The proposed model demonstrates high accuracy in predicting age stages using facial images, indicating improvements in prediction performance across varying conditions.

Age Stage Prediction Using Light Convolution Neural Network

Discussion

Member takes

Implication

Key Points

Cite This Study

Age Stage Prediction Using Light Convolution Neural Network

Discussion

Member takes

Implication

Key Points

Cite This Study

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