Age and Gender Prediction Techniques Using Artificial Intelligence, Machine Learning, and Deep Learning: A Comprehensive Review

Volume: 10    Issue: 2

Year of Publication: 2025

Pages: 70-74

Authors: Harshal Rokade, Dr. Anil Warbhe, Samiksha Kshirsagr, Aryan Dongre

DOI: https://doi.org/10.5281/zenodo.17784742

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Abstract

This review synthesizes on age and gender prediction from facial images, focusing exclusively on methodologies leveraging artificial intelligence (AI), machine learning (ML), and deep learning (DL). The purpose is to evaluate the evolution and efficacy of these techniques in addressing challenges such as unconstrained imaging conditions, dataset variability, and real-time applicability. AI approaches provide foundational frameworks for feature extraction and classification, while ML methods emphasize traditional classifiers like support vector machines (SVM) and neural networks for robust pattern recognition. DL, particularly convolutional neural networks (CNNs), dominates recent advancements, incorporating transfer learning, multi-task learning, and optimization strategies to achieve superior accuracies. Key findings reveal DL models, such as VGG-16, ResNet-50, and GoogLeNet, yielding gender classification accuracies up to 99.3% and mean absolute errors (MAE) as low as 2.68 years for age estimation on datasets like MORPH-II, Adience, and UTKFace. ML techniques, including SVM and artificial neural networks (ANN), offer accuracies around 83-96% but struggle with large-scale variations. Common datasets include Adience for unconstrained scenarios and MORPH-II for longitudinal aging data. Preprocessing steps like histogram equalization and data augmentation enhance performance, with evaluation metrics favoring accuracy, F1-score, and MAE. Despite progress, research gaps persist in handling occlusions (e.g., masks), ethnic diversity, and computational efficiency for mobile deployment. This review underscores DL\"s transformative role while highlighting the complementary strengths of ML for resource-constrained environments.

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Keywords

Age Estimation, Gender Classification, Convolutional Neural Networks, Transfer Learning, Facial Recognition, Deep Learning