Machine Learning Approach for Hairstyle Recommendation
According to aesthetic evaluations, hair is the most unique feature which can enhance the facial features of a person. Beauty experts have identified that 70% of overall face appearance completely depends on the haircut or hairstyle. The physical attributes such as the haircut is a major determinant of women's psychology. This is the essence of why a haircut which is matching a woman's face is necessary articulation. But selecting the right haircut or hairstyle is one of the most difficult decisions to take in a woman's life. This paper presents a novel framework to select the most suitable hairstyle or haircut by classifying the face shape. The author considers the shape of the face, beauty experts knowledge related to hair cuts and hairstyles and the length of the hair to develop a model to recommend the most suitable hairstyle or haircut. The author focused to recommend the haircuts and hairstyles for women which is a subsection of this large research area. According to beauty experts identifying the shape of the face is the most important step before selecting the right hairstyle or haircut. The proposed model has the ability to classify the face shape when a user uploaded a portrait of herself. Machine Learning libraries were used to identify the landmarks of the face image and classify the face in the correct shape. Naive Bayes classification algorithm has used to recommend the most suitable hairstyle or haircut according to the detected face shape. hair length and information collected from the hair experts. User has given an option to share the recommended hair style or haircut with the beautician via "The Beauty Quest" Salon network platform. Five thousand images were trained, and python language has used as the programming language. The accuracy of the face shape classification model is 91% and the accuracy of the hair recommendation is also 83%
2020 5th International Conference on Information Technology Research (ICITR)