基于深度学习的年龄估计与性别分类在定向广告中的应用

摘要

本文提出了一种基于深度学习的新方法，用于从面部图像中同时进行年龄和性别分类，旨在提升定向广告活动的有效性。我们设计了一种定制的卷积神经网络（CNN）架构，针对这两项任务进行了优化，该架构充分利用了面部特征中年龄与性别信息之间的内在关联。与现有方法通常独立处理这些任务不同，我们的模型学习共享表示，从而提高了性能。该网络在一个大规模、多样化的面部图像数据集上进行训练，这些图像经过精心预处理，以确保对光照、姿态和图像质量变化的鲁棒性。实验结果显示，性别分类准确率显著提升，达到95%，年龄估计的平均绝对误差为5.77岁，表现具有竞争力。重要的是，我们分析了不同年龄组的表现，识别出在准确估计年轻人年龄方面存在的特定挑战。这一分析揭示了针对性的数据增强和模型优化的必要性，以解决这些偏差。此外，我们探讨了不同CNN架构和超参数设置对整体性能的影响，为未来研究提供了宝贵的见解。

English

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.

基于深度学习的年龄估计与性别分类在定向广告中的应用

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

摘要

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