Deep Learning-Based Age and Gender Detection Using CNN and Hybrid Machine Learning Approaches

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Deep learning finds accurate age and gender detection from facial images, suggesting hybrid models enhance performance.

The model consistently achieves high accuracy in detecting age and gender across different age brackets.
Using the UTKFace and FG-NET datasets, the approach ensures diverse representation for robust evaluation.
Convolutional neural networks combined with support vector machines improve feature extraction and classification.
The study's framework highlights the potential for scalable, real-time age and gender detection systems.

Sambo et al. (2025) studied this question.

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