DreamyMirror A Machine Learning-Based Personalized Outfit Recommendation System Integrating Skin Tone Classification and Size
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Sri Lanka Technology Campus
Abstract
Due to the increasing popularity of e-commerce, buying
has undergone a tremendous shift and belongs to a
different world. ” But there is still some difficulties with
personalization when it comes to the proposed size and
the choice of the color taking into consideration the skin
color of the person. In this paper, the authors introduce
DreamyMirror, a novel system for recommending outfits
based on the individual user’s skin tone and size, along
with allowing for virtual fitting. Based on a skin tone
classifier CNN model built from CelebA database and a
pre-trained Pose_iter_440000 of OpenPose. size
prediction caffemodel, DreamyMirror provides unique
suggestions of outfits according to the appearance of the
person. The system also contains the Virtual Try on
system wherein users can anticipate what the selected
apparels look like at a glance. The proposed system can
automatically recognize skin tone with an overall
classification accuracy of 92% and the size prediction
accuracy is found to be 95%. Moreover, the user
engagement metrics also show an 85% level of
satisfaction with the features – this is about the virtual
try-on. This work shows how the application of AI in
fashion retail can help better engage the customer
through a truly individualized shopping experience and,
at the same time, minimize the return rate because of the
wrong size or color choice.
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DreamyMirror A Machine Learning-Based Personalized Outfit Recommendation System Integrating Skin Tone Classification and Size S. J. A. S. K. Serasinghe Department of Data Science, Sri Lanka Technological Campus Meepe, Padukka, Sri Lanka shermilas@sltc.ac.lk P. V. C. G. Jayarathne Department of Data Science, Sri Lanka Technological Campus Meepe, Padukka, Sri Lanka chamindug@sltc.ac.lk