International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Powered Personal Style Recommender Using Image Analysis |
|---|---|
| ABSTRACT | The AI-Powered Personal Style Recommender Using Image Analysis and Deep Learning improves the fashion selection experience. It uses artificial intelligence to understand clothing through images and provide tailored outfit recommendations. Unlike traditional systems that depend on purchase history or text descriptions, this system analyzes garment images with deep learning models to identify important visual features like color combinations, texture, and style patterns. Users can upload clothing images, receive coordinated outfit suggestions, and see styling combinations. By integrating deep learning for feature extraction, similarity matching, and user preference modeling, the platform provides precise and personalized recommendations. It has secure data handling methods and scalable cloud storage to ensure reliability and performance. This recommender focuses on the user, enhancing personalization, visual compatibility, and decision-making in digital fashion platforms. |
| AUTHOR | DR.V.PRASANNA SRINIVASAN, R. HEMAVATHY, S. ISAIVASHINI, V. KARTHIGA, P. KAVIPRIYA Professor, Department of Information Technology, R.M.D. Engineering College, Chennai, Tamil Nadu, India Student, Department of Information Technology, R.M.D. Engineering College, Chennai, Tamil Nadu, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403037 |
| pdf/37_AI-Powered Personal Style Recommender Using Image Analysis.pdf | |
| KEYWORDS | |
| References | [1] He, K., et al. "Deep Residual Learning for Image Recognition." (The foundation for the ResNet-50 mentioned in your architecture). [2] Isola, P., et al. "Image-to-Image Translation with Conditional Adversarial Networks." (The foundation for the GAN logic used in your virtual try-on). [3] Zhan, H., et al. "Fashion Retrieval via Joint Deep Learning of Attributes and Semantic Parts." (Supports your Feature Extraction and Similarity Engine). [4] Liu, Z., et al. "DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations." (Supports your use of Clothing Datasets) |
