Automatic Face Shape Classification Via Facial Landmark Measurements

Abstract

This paper tackles the sensitive subject of face shape identification via near neutral-pose 2D images of human subjects. The possibility of extending to 3D facial models is also proposed, and would alleviate the need for the neutral stance. Accurate face shape classification serves as a vital building block of any hairstyle and eye-wear recommender system. Our approach is based on extracting relevant facial landmark measurements and passing them through a naive Bayes classifier unit in order to yield the final decision. The literature on this subject is particularly scarce owing to the very subjective nature of human face shape classification. We wish to contribute a robust and automatic system that performs this task and highlight future development directions on this matter. Received by the editors: 15 September 2021. 2010 Mathematics Subject Classification. 68T45. 1998 CR Categories and Descriptors. I.2.10 Computing Methodologies [ARTIFICIAL INTELLIGENCE]: Vision and Scene Understanding – Modeling and recovery of physical attributes.

Keywords

• artificial intelligence, computer vision, naive Bayes, face shape,facial landmarks.