Deep Learning-Based Age Estimation and Gender Deep Learning-Based Age Estimation and Gender Classification for Targeted Advertisement

• URL: http://arxiv.org/abs/2507.18565v1
• Date: Thu, 24 Jul 2025 16:41:26 GMT
• Title: Deep Learning-Based Age Estimation and Gender Deep Learning-Based Age Estimation and Gender Classification for Targeted Advertisement
• Authors: Muhammad Imran Zaman, Nisar Ahmed,
• Abstract summary: This paper presents a novel deep learning-based approach for simultaneous age and gender classification from facial images.<n>We propose a custom Convolutional Neural Network (CNN) architecture, optimized for both tasks.<n>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.
• Score: 3.376269351435396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Exploring the Feasibility of Deep Learning Techniques for Accurate Gender Classification from Eye Images [1.6114012813668932]
  periocular region refers to the area surrounding the eye, including the eyelids, eyebrows, and the region between them.<n>It contains valuable visual cues that can be used to extract key features for gender classification.<n>This paper introduces a sophisticated Convolutional Neural Network (CNN) model that utilizes color image databases to evaluate the effectiveness of the periocular region for gender classification.
  arXiv  Detail & Related papers  (2025-07-31T19:52:03Z)
• Bridging the gap in FER: addressing age bias in deep learning [0.562479170374811]
  We study age-related bias in deep FER models, with a particular focus on the elderly population.<n>Using Explainable AI (XAI) techniques, we identify systematic disparities in expression recognition and attention patterns.<n>Results show consistent improvements in recognition accuracy for elderly individuals.
  arXiv  Detail & Related papers  (2025-07-10T11:07:13Z)
• A Hybrid Transformer-Sequencer approach for Age and Gender classification from in-wild facial images [1.7556999242499645]
  This paper proposes a hybrid model that combines self-attention and BiLSTM approaches for age and gender classification problems. An improvement of approximately 10percent and 6percent over the state-of-the-art implementations for age and gender classification, respectively, are noted for the proposed model.
  arXiv  Detail & Related papers  (2024-03-19T06:40:06Z)
• Gender Biases in Automatic Evaluation Metrics for Image Captioning [87.15170977240643]
  We conduct a systematic study of gender biases in model-based evaluation metrics for image captioning tasks. We demonstrate the negative consequences of using these biased metrics, including the inability to differentiate between biased and unbiased generations. We present a simple and effective way to mitigate the metric bias without hurting the correlations with human judgments.
  arXiv  Detail & Related papers  (2023-05-24T04:27:40Z)
• Time Series Contrastive Learning with Information-Aware Augmentations [57.45139904366001]
  A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples. How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question. We propose a new contrastive learning approach with information-aware augmentations, InfoTS, that adaptively selects optimal augmentations for time series representation learning.
  arXiv  Detail & Related papers  (2023-03-21T15:02:50Z)
• LAE : Long-tailed Age Estimation [52.5745217752147]
  We first formulate a simple standard baseline and build a much strong one by collecting the tricks in pre-training, data augmentation, model architecture, and so on. Compared with the standard baseline, the proposed one significantly decreases the estimation errors. We propose a two-stage training method named Long-tailed Age Estimation (LAE), which decouples the learning procedure into representation learning and classification.
  arXiv  Detail & Related papers  (2021-10-25T09:05:44Z)
• FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild [50.8865921538953]
  We propose a method to explicitly incorporate facial semantics into age estimation. We design a face parsing-based network to learn semantic information at different scales. We show that our method consistently outperforms all existing age estimation methods.
  arXiv  Detail & Related papers  (2021-06-21T14:31:32Z)
• Age Gap Reducer-GAN for Recognizing Age-Separated Faces [72.26969872180841]
  We propose a novel algorithm for matching faces with temporal variations caused due to age progression. The proposed generative adversarial network algorithm is a unified framework that combines facial age estimation and age-separated face verification.
  arXiv  Detail & Related papers  (2020-11-11T16:43:32Z)
• Age and Gender Prediction From Face Images Using Attentional Convolutional Network [6.3344832182228]
  We propose a deep learning framework, based on the ensemble of attentional and residual convolutional networks, to predict gender and age group of facial images with high accuracy rate. Our model is trained on a popular face age and gender dataset, and achieved promising results.
  arXiv  Detail & Related papers  (2020-10-08T06:33:55Z)
• Enhancing Facial Data Diversity with Style-based Face Aging [59.984134070735934]
  In particular, face datasets are typically biased in terms of attributes such as gender, age, and race. We propose a novel, generative style-based architecture for data augmentation that captures fine-grained aging patterns. We show that the proposed method outperforms state-of-the-art algorithms for age transfer.
  arXiv  Detail & Related papers  (2020-06-06T21:53:44Z)
• Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study [67.3961439193994]
  We introduce the most comprehensive, large-scale dataset of facial images and videos to date. The data are manually annotated in terms of identity, exact age, gender and kinship. A method to debias network embeddings is introduced and tested on the proposed benchmarks.
  arXiv  Detail & Related papers  (2020-05-15T00:14:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.