System for automatic personalization of head-related transfer functions based on computer vision, photo-anthropometry, and inference from a database

Abstract

A software system and method for automatic personalization of head related transfer functions (HRTF) is presented. The system pursues the objective of personalizing HRTF using the anthropometry of the subject, as proposed originally by [Zotkin et al. WASPAA 2003, 157–160], which in this work, is measured automatically from a set of photographs of the subject. The system operates in three stages. First, a computer vision algorithm, known as active shape models, is used over the portraits to recognize and adjust geometric form profiles of the ears, head, and torso of the subject. Then, anthropometry is performed by measurement of pixel distances between specific points in the model, and then converted into metric units. Finally, HRTF are estimated from the anthropometry using a choice of three methods, whose performance is compared: (1) HRTF selection of the best anthropometric match in the CIPIC database, (2) HRTF synthesis by multiple linear regression and principal component analysis, and (3) HRTF synthesis using an artificial neural network. Results are analyzed, concluding that automatic personalization of HRTF is attainable automatically from the subject portraits, using computer vision and inference from a database. Further analysis, however, reveals the need for more complete HRTF and anthropometric databases.