Abstract: StyleSage is an innovative project that leverages advanced machine learning techniques to revolutionize hairstyle recommendations. By analysing user-provided face images, whether through image uploads or real-time camera input, the system accurately identifies the individual's facial shape from five categories: long, square, oval, heart, and round. This critical initial step sets the stage for tailored suggestions. Utilizing a diverse dataset of hairstyles, the model then selects the most fitting options based on the user's facial shape, resulting in a curated list of six hairstyle images. Beyond saving users time and experimentation, StyleSage enhances their confidence by offering personalized recommendations that harmonize with their unique features, seamlessly combining technology and beauty. Incorporating sophisticated image processing and machine learning algorithms, StyleSage bridges the gap between technology and personal aesthetics. Its ability to discern facial attributes enables precise facial shape classification, which, in turn, drives the selection of hairstyles that truly complement the individual. By fostering a synergy between data-driven predictions and the art of beauty, StyleSage empowers users to confidently explore hairstyles aligned with their distinct characteristics, marking a significant advancement beyond conventional recommender systems. Ultimately, StyleSage exemplifies the marriage of machine learning and personal expression, reinventing how we approach hairstyling choice

Keywords: Image Processing, Facial Attribute Analysis, Machine Learning Algorithms, Data-driven Predictions, Recommender Systems

| DOI: 10.17148/IARJSET.2023.10845