An automated approach to female body-type classification for fashion style recommendations using computer-vision and machine learning
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Date
2024
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Faculty of Science, University of Kelaniya Sri Lanka
Abstract
This research explores a computer vision-based approach to automating female body-type classification with the goal of enhancing personalized fashion recommendations in the online retail industry. By tailoring style suggestions to individual body types, the system aims to improve customer satisfaction and reduce return rates. The system utilizes deep learning techniques to analyze full-body images and classify them into five body-types: apple, inverted triangle, hourglass, pear, and rectangle. Traditional body-type classification methods often require detailed body measurements or complex 3D modeling, posing challenges in terms of user-friendliness and accessibility. This study highlights the advantages of deep learning and transfer learning, which enable the extraction of complex features from images, facilitating accurate and efficient classification without requiring specialized hardware or extensive user input. The model was trained using a dataset of 560 full-body images of female participants aged 20 to 35 years, representing a young adult demographic. To identify the most effective model for this task, the research compared the performance of various machine learning algorithms, including machine learning models, deep learning CNN architectures, and transfer learning models such as Xception, ResNet50, MobileNetV2, and VGG16. Accuracy and model stability were the primary evaluation criteria. The VGG16 model emerged as the best-performing classifier, achieving an accuracy rate of 83.50%. It was trained on 224x224x3 images over 100 epochs with a batch size of 32, using the Adam optimizer and a learning rate of 1e-5. Categorical cross-entropy was used to measure model performance, ensuring optimal parameter adjustments. This model was integrated into both a mobile application, and a web application. These applications allow users to upload images, predict their bodytype, and receive personalized fashion suggestions. In addition to performance metrics like precision, recall, F1-score, and accuracy, the system was validated through a user feedback survey. This survey gathered responses from users who interacted with the web application and served as a human validation metric. The classification model demonstrated performance, particularly with high F1-scores for the Inverted Triangle (0.91) and Apple Shape (0.82) body types. Hourglass and Pear shapes, while moderately accurate, showed lower precision and recall. User feedback from 60 respondents indicated high satisfaction with the system: 94% expressed satisfaction with the classification accuracy, 85.5% emphasized the importance of body type in fashion selection, and 73% reported satisfaction with the personalized fashion suggestions. These insights confirm the system's reliability in real-world applications. While this research demonstrates satisfactory results, limitations exist. The utilized dataset is relatively small, and the classification is limited to five body-types. Additionally, the fashion suggestions are text-based rather than image-based. Future work will focus on expanding the dataset to improve classification accuracy, incorporating all eight recognized female body types, and integrating image-based fashion suggestions to enhance usability. This research lays the foundation for future advancements in AI-driven fashion recommendation systems, contributing to a more personalized, and efficient fashion retail experience.
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Keywords
Computer-Vision, Female Body-Type Classification, Fashion Suggestions, Online Shopping, Transfer Learning Models
Citation
Navodya H. K. S.; Sandaruwan K. D. (2024), An automated approach to female body-type classification for fashion style recommendations using computer-vision and machine learning, Proceedings of the International Conference on Applied and Pure Sciences (ICAPS 2024-Kelaniya) Volume 4, Faculty of Science, University of Kelaniya Sri Lanka. Page 137