Deep Learning-Based Age Estimation and Gender Deep Learning-Based Age
  Estimation and Gender Classification for Targeted Advertisement
This paper presents a novel deep learning-based approach for simultaneous age
and gender classification from facial images, designed to enhance the
effectiveness of targeted advertising campaigns. We propose a custom
Convolutional Neural Network (CNN) architecture, optimized for both tasks,
which leverages the inherent correlation between age and gender information
present in facial features. Unlike existing methods that often treat these
tasks independently, our model learns shared representations, leading to
improved performance. The network is trained on a large, diverse dataset of
facial images, carefully pre-processed to ensure robustness against variations
in lighting, pose, and image quality. Our experimental results demonstrate a
significant improvement in gender classification accuracy, achieving 95%, and a
competitive mean absolute error of 5.77 years for age estimation. Critically,
we analyze the performance across different age groups, identifying specific
challenges in accurately estimating the age of younger individuals. This
analysis reveals the need for targeted data augmentation and model refinement
to address these biases. Furthermore, we explore the impact of different CNN
architectures and hyperparameter settings on the overall performance, providing
valuable insights for future research.
688332fe846d2e78b7548b7c
Muhammad Imran Zaman
claimed_verified
2025-07-25T08:38:23.842Z
659d5355d09adee457236d53
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/y1W6Co4jIYB95Cx6Tjrsd.jpeg
Muhammad Imran Zaman
user
ImranzamanML
688332fe846d2e78b7548b7d
Nisar Ahmed
Deep Learning-Based Age Estimation and Gender Deep Learning-Based Age
  Estimation and Gender Classification for Targeted Advertisement
This paper presents a novel deep learning-based approach for simultaneous age
and gender classification from facial images, designed to enhance the
effectiveness of targeted advertising campaigns. We propose a custom
Convolutional Neural Network (CNN) architecture, optimized for both tasks,
which leverages the inherent correlation between age and gender information
present in facial features. Unlike existing methods that often treat these
tasks independently, our model learns shared representations, leading to
improved performance. The network is trained on a large, diverse dataset of
facial images, carefully pre-processed to ensure robustness against variations
in lighting, pose, and image quality. Our experimental results demonstrate a
significant improvement in gender classification accuracy, achieving 95%, and a
competitive mean absolute error of 5.77 years for age estimation. Critically,
we analyze the performance across different age groups, identifying specific
challenges in accurately estimating the age of younger individuals. This
analysis reveals the need for targeted data augmentation and model refinement
to address these biases. Furthermore, we explore the impact of different CNN
architectures and hyperparameter settings on the overall performance, providing
valuable insights for future research.
A custom CNN architecture for simultaneous age and gender classification from facial images improves performance by learning shared representations, achieving high gender classification accuracy and competitive age estimation.
Convolutional Neural Network (CNN) age classification gender classification facial features shared representations data augmentation hyperparameter settings