3D Human Head Shape Variation by Using Principal Component Analysis

Abstract

In traditional anthropometry, people adopt the concept of percentile of some critical dimensions, e.g., height. However, percentile has been criticized and its applicability in product design is controversial. Another popular concept in fitting design is sizing, which means to classify human samples into pre-defined categories. Conventionally, sizing scheme usually adopt no more than four dimensions to set up dozens of complex grading charts. However, human head is in 3D form, and limited dimensions can’t represent its whole morphologic variation. By the aid of 3D scan technology, there have appeared numerous large-scale 3D human body surveys, as an example, the latest and largest 3D human body survey of Chinese minors conducted by China National Institute of Standardization in the last decade. We used Principal Component Analysis (PCA) as our approach and analyzed 100 3D human head models (all males) and compared their shape variation. The sample data used for our study were taken from 3D human body survey of Chinese minors conducted by China National Institute of Standardization. Our results showed that four principal components described more than 90% of the total variation in the sample. Models of the 3D human head lying on the hyper-ellipsoid constituted by principal component axis have also been re-constructed, using the sample mean and principal components, and they are used to illustrate the variation in human head shape of the sample population, to generate new human head shapes and to reconstruct different human head shapes rapidly. Furthermore, the shape variation carried by each principal component combined distinct factors, e.g., height variation, width variation or depth variation. It is hard to differentiate the specific meaning of each principal component, which made PCA difficult to be used for product designers, tailors and other engineers. Therefore we extended PCA method with a novel regression model to explore the semantic attributes of each principal component. Our method can achieve 3D shape variation quantification efficiently, intuitively and accurately. Experimental results show that PCA on 3D point cloud to realize 3D human head shape variation is an effective method. This method can also find applications in parametric human body modeling, which will greatly reduce the cost of animation and the time of human modeling.