Facial Shape Analysis and Accessory Recommendation: A Human-Centric AI Approach
Sanjay Reddy Komatireddy, Karnam Meghana, Venkataramaiah Gude, G. Ramesh
Abstract
In this digital world, the fusion of Artificial Intelligence (AI) and Human-Computer Interaction (HCI) opens new avenues for enhancing user experiences through personalization. This research centers on the realm of facial shape analysis and its applications in providing tailored accessory recommendations, harnessing AI techniques such as Machine Learning (ML) algorithms to offer customized suggestions. The primary objective is to create an intelligent system adept at recognizing facial shapes and delivering accessory recommendations that harmonize seamlessly with an individual’s unique facial characteristics. To accomplish this, advanced Artificial Intelligence (AI) algorithms specialized in facial shape recognition and analysis have been utilized. These algorithms include K-Means, DBSCAN, and Agglomerative Clustering, each playing a crucial role in the process. The system’s recommendations are founded on a comprehensive analysis that combines both quantitative data and qualitative insights. The findings underscore the effectiveness of AI-driven facial shape analysis in personalizing accessory recommendations. This study highlights AI’s capacity to elevate user experiences and showcases the intersection of technology and fashion within HCI. As a contribution to the evolving field of AI and HCI, this research emphasizes the importance of user centered solutions. Moreover, the results act as a foundational pillar for future research in personalized recommendations and user interfaces, effectively bridging the gap between AI advancements and human-centric design.