Implementation of a Brass Instrument Player Classification Model Using Facial Recognition with Two-Stage Classification Combining SVM and CNN

In wind music, beginners often have difficulty selecting an instrument that suits them. This is particularly true for brass instruments, as physical characteristics such as lip shape and bone structure influence playing aptitude, making appropriate instrument selection crucial. In this study, we developed a model for identifying suitable brass instrument players using facial recognition with a two-stage classification approach. This approach combines global classification using a Support Vector Machine (SVM) and local classification using a Convolutional Neural Network (CNN). Specifically, we implemented a deep learning model that estimates the appropriate instrument on the basis of lip shape and bone structure by analyzing profile photos of professional orchestra brass musicians. The experimental results demonstrated the effectiveness of the proposed classification model, achieving an accuracy of 72% in classification. Through this study, we aim to create an environment in which beginners are assigned suitable instruments, thereby enhancing their motivation.