Artificial Intelligence and Technology
Mehjabin Prodhan Faiza
ABSTRACT
This paper presents a real-time facial recognition system tailored for advertising personalization. The proposed platform analyzes facial attributes: age, gender, and emotion from live image or video feeds to dynamically deliver targeted advertisements. The system leverages deep learning models (Convolutional Neural Networks and recurrent networks) to perform robust face analysis and couples these predictions with a content-based recommendation engine. We implement the solution as a web application using Flask, TensorFlow, and OpenCV, integrating pre-trained CNN architectures (ResNet50, VGG16, GoogleNet) and an LSTM for temporal emotion modeling. The system is trained on large-scale face datasets (UTKFace for age/gender, FER2013 for expressions) and achieves high accuracy in demographic and emotion classification, which translates into effective ad recommendations. To address ethical considerations, we incorporate privacy-by-design principles, no personal identifiers are stored, and all processing is done in- memory, and we align with legal standards (e.g. GDPR) to protect user data. Experimental evaluation demonstrates 96% gender classification accuracy, a 4-year mean absolute error in age estimation, and an average F1-score of 0.72 in emotion recognition, enabling a recommendation match F1 of 0.87. These results highlight the potential of deploying deep learning-based facial analytics in commercial advertising for enhanced user engagement, while maintaining responsible AI practices and user trust.
