基於深度學習的年齡估計與性別分類於定向廣告之應用

摘要

本文提出了一種基於深度學習的新方法，用於從面部圖像中同時進行年齡和性別分類，旨在提升定向廣告活動的效果。我們設計了一種定制的卷積神經網絡（CNN）架構，針對這兩項任務進行了優化，充分利用了面部特徵中存在的年齡與性別信息之間的內在關聯。與現有方法通常將這些任務獨立處理不同，我們的模型學習共享表示，從而提高了性能。該網絡在一個大型且多樣化的面部圖像數據集上進行訓練，並經過仔細的預處理，以確保對光照、姿態和圖像質量變化的魯棒性。實驗結果顯示，性別分類準確率顯著提升，達到95%，而年齡估計的平均絕對誤差為5.77年，表現具有競爭力。重要的是，我們分析了不同年齡組的性能，發現了在準確估計年輕人年齡方面的特定挑戰。這一分析揭示了需要針對性的數據增強和模型改進來解決這些偏差。此外，我們探討了不同CNN架構和超參數設置對整體性能的影響，為未來研究提供了寶貴的見解。

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.

基於深度學習的年齡估計與性別分類於定向廣告之應用

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

摘要

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