Enhancing Fashion Recommendations with Facial Recognition: A Gender and Age Prediction Perspective

Abstract

This research paper presents a real-time system for age and gender estimation integrated into a fashion recommendation context. Leveraging deep learning models for facial detection and classification, alongside a Random Forest-based recommender system for fashion items, the system provides personalized recommendations based on user age, gender, and preferences. The methodology involves detecting faces in video streams, extracting facial regions, and preprocessing them for age and gender estimation using pre-trained CNN models. Fashion recommendations are then generated by assigning weighted scores to items based on various attributes and training a Random Forest regressor. Implementation details, including the use of Python, OpenCV, and machine learning libraries, are discussed. Evaluation metrics for age and gender detection models, as well as recommendation system performance, are outlined. The paper concludes with future directions and challenges, emphasizing enhancements in age and gender estimation accuracy, integration with e-commerce platforms, and the importance of data privacy and computational resources. Keywords — Age and gender estimation, Facial detection, Deep learning, Fashion recommendation, Random Forest.