Ancient thangka Buddha face recognition based on the Dlib machine learning library and comparison with secular aesthetics

Abstract

Presently, face recognition technology is rapidly advancing and has emerged as a crucial field of research. Thangka, being a significant repository of Buddhist imagery, encompass a vast amount of Buddha image data depicting diverse Buddhist themes from various historical epochs. Accurate recognition of facial features in these Buddha images is particularly significant in comprehending the historical evolution of thangka, especially the facial features correlation between Buddha society and secular society. Hence, in this study, 68 facial feature points was employed to obtain using the Dlib deep learning library, from which 16 facial geometric feature indices were derived. These indices served as the foundation for the establishment of a facial measurement standard and aesthetic evaluation index for thangka Buddha. A meticulous evaluation and identification of thangka facial details spanning nearly a millennium were conducted, and the transformation of thangka facial features was analyzed and deliberated from a secular aesthetic perspective. Upon conducting this study, it was discovered that: (1) The deep learning library exhibited effective performance in identifying facial characteristics in thangka Buddha images, and the facial evaluation index proved to be a reliable tool for evaluating both measurement standards and aesthetics. (2) The facial measurement standards depicted in thangka Buddha images have evolved and become increasingly standardized over time, maintaining a highly symmetrical aesthetic. (3) The aesthetic of thangka facial features draw upon the secular Tibetan face as their primary reference(Euclidean distance is 0.42), however, during the 17-19th centuries, Han Chinese facial features were gradually incorporated(Euclidean distance is 0.492), and the degree of fusion between Han Chinese and Tibetan facial aesthetics has become more profound.