Abstract:
The PerfectFit solution addresses the challenge of accurately classifying women's body shapes
from the uploaded images to provide personalized fashion recommendations. Traditional methods
relying on manual measurement or surface classifiers are likely to fail on the richness of real
images, with the variations of pose, lighting, and background. Existing fashion recommendation
systems mostly ignore useful aspects such as skin tone and occasion that limit their personalization
and user satisfaction. The basic problem therefore is to design an automatic, strong, and
interpretable system that is capable of identifying body shapes well and merging multiple attributes
of users to offer personalized advice on dressing.
To solve this problem, PerfectFit employs a deep learning approach founded on the MobileNetV2
convolutional neural network model. MobileNetV2 was chosen based on its trade-off between
accuracy and computational expense that qualifies it for use in real-time scenarios. The model
relies on transfer learning using the initialization of pretrained ImageNet weights followed by fine
tuning on a judiciously selected dataset of body shape images appropriately labeled. The
architecture includes bottleneck residual blocks and depthwise separable convolutions to reduce
model size without sacrificing performance. Additional layers such as Global Average Pooling
and a Dense softmax output layer were added to classify five distinct body shape categories.
Complementary modules for skin tone detection and explainable AI, powered by OpenAI’s GPT
3.5, were integrated to enhance recommendation personalization and transparency.
