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ZENODO
Article . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
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Aesthetiq: AI-Powered Aesthetic Analysis And Personalized Styling Recommendation System

Authors: Sree Vishal G; Sarika K; Dr. K. Geetha;

Aesthetiq: AI-Powered Aesthetic Analysis And Personalized Styling Recommendation System

Abstract

The rapid advancement of digital technologies and the widespread use of social media platforms have significantly influenced the way individuals present themselves in modern society. Personal appearance, grooming, and aesthetic presentation have become essential aspects of self-expression and identity. However, selecting appropriate styles, outfits, and visual themes that align with individual preferences and current trends remains a complex and time-consuming task. Traditional approaches rely heavily on manual browsing, personal judgment, and external opinions, which often lack accuracy, consistency, and personalization.To address these challenges, this paper presents Aesthetiq, an Artificial Intelligence-based aesthetic analysis and personalized styling recommendation system. The proposed system is designed as a web-based application that leverages machine learning techniques to analyze user inputs such as facial images, style preferences, and visual attributes. The system performs preprocessing, feature extraction, and classification to identify suitable aesthetic categories and generate personalized recommendations.The architecture of the system consists of a frontend interface for user interaction, a backend server for processing and communication, and an AI module for performing analysis. The database stores user data, input images, and analysis results to enable efficient retrieval and history tracking. The system ensures real-time processing and provides visually interpretable outputs through an interactive dashboard.Experimental evaluation indicates that the proposed system achieves improved accuracy and performance compared to traditional methods. The system enhances user decision-making, reduces effort, and provides tailored recommendations that align with individual preferences and modern trends.

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average