Abstract:
"Aim of this study is to implement a CNN-based hairstyle recommendation system that can identify various face shapes. Modern machine learning methods are used by this system, which is develop with open source components like TensorFlow and Keras, to provide individualized hair recommendations.
The CNN model is used to predict user's face shape based on their image after being trained on a library of photographs with different face shapes. The user is then given recommendations for hairstyles that might go well with their expected facial shape. The user-friendly interface that presents the hairstyle recommendations contains interactive features that allow the user to select hair color, style, and length. The system's accuracy, precision, and recall are among the criteria used to assess its performance. The outcomes show how machine learning techniques can be used to provide individualized grooming recommendations. The precision and accuracy of the system are essential to the dependability of the suggestions made.
In order to provide more accurate style recommendations, this research offers a novelty approach to hairstyle recommendations which can be enhanced using factors like hair texture and color. This device could enhance the hair salon experience for customers. Salons, assisting patrons in selecting haircuts which highlight different tastes and attributes. It might be included in apps for wellness and beauty, giving people along with improved, customized glamour treatment. All in all, the study presents new opportunities for tailored suggestions and demonstrates the applicability of machine learning techniques in the cosmetics business. It illustrates how recommendation systems and image categorization may be combined for an improved customized elegance experience. Subsequent investigations may concentrate on enhancing the structure's functionalities with supplementary attributes while generating a stronger dataset to model training."
