用深度学习同时识年龄性别，提升广告精准度
Deep Learning-Based Age Estimation and Gender Deep Learning-Based Age Estimation and Gender Classification for Targeted Advertisement
- 设计联合网络，利用脸型中年龄与性别的关联特征
- 性别识别准确率达95%，年龄估计误差为5.77年
- 发现年轻人年龄估计偏差大，适合广告与模型优化研究
本文提出一种基于深度学习的联合年龄与性别分类方法，旨在提升定向广告效果。设计了一种定制化的卷积神经网络（CNN）架构，通过挖掘面部特征中年龄与性别间的内在关联，实现双任务协同学习。相比传统独立处理方式，该模型能提取共享表征，显著提升性能。网络在大规模、多样化的面部图像数据集上训练，并经过光照、姿态和图像质量等预处理以增强鲁棒性。实验表明，性别分类准确率达到95%，年龄估计的平均绝对误差为5.77年。进一步分析显示，年轻群体的年龄估计存在较大偏差，提示需针对性加强数据增强与模型优化。此外，还系统评估了不同CNN结构与超参数对整体性能的影响，为后续研究提供参考。
原文摘要 · Abstract (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.
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