Modelling facial dynamics change as people age

In the recent years, increased research activity in the area of facial ageing modelling has been recorded. This interest is attributed to the potential of using facial ageing modelling techniques for a number of different applications, including age estimation, prediction of the current appearance of missing persons, age-specific human-computer interaction, computer graphics, forensic applications, and medical applications. This thesis describes a general AAM model for modelling 4D (3D dynamic) ageing and specific models to map facial dynamics as people age. A fully automatic and robust pre-processing pipeline is used, along with an approach for tracking and inter-subject registering of 3D sequences (4D data). A 4D database of 3D videos of individuals has been assembled to achieve this goal. The database is the first of its kind in the world. Various techniques were deployed to build this database to overcome problems due to noise and missing data. A two-factor (age groups and gender) analysis of variance (MANOVA) was performed on the dataset. The groups were then compared to assess the separate effects of age on gender through variance analysis. The results show that smiles alter with age and have different characteristics between males and females. We analysed the rich sources of information present in the 3D dynamic features of smiles to provide more insight into the patterns of smile dynamics. The sources of temporal information that have been investigated include the varying dynamics of lip movements, which are analysed to extract the descriptive features. We evaluated the dynamic features of closed-mouth smiles among 80 subjects of both genders. Multilevel Principal Components Analysis (mPCA) is used to analyse the effect of naturally occurring groups in a population of individuals for smile dynamics data. A concise overview of the formal aspects of mPCA has been outlined, and we have demonstrated that mPCA offers a way to model the variations at different levels of structure in the data (between and within group levels)

What's in a smile? Initial analyses of dynamic changes in facial shape and appearance

Single-level principal component analysis (PCA) and multi-level PCA (mPCA) methods are applied here to a set of (2D frontal) facial images from a group of 80 Finnish subjects (34 male; 46 female) with two different facial expressions (smiling and neutral) per subject. Inspection of eigenvalues gives insight into the importance of different factors affecting shapes, including: biological sex, facial expression (neutral versus smiling), and all other variations. Biological sex and facial expression are shown to be reflected in those components at appropriate levels of the mPCA model. Dynamic 3D shape data for all phases of a smile made up a second dataset sampled from 60 adult British subjects (31 male; 29 female). Modes of variation reflected the act of smiling at the correct level of the mPCA model. Seven phases of the dynamic smiles are identified: rest pre-smile, onset 1 (acceleration), onset 2 (deceleration), apex, offset 1 (acceleration), offset 2 (deceleration), and rest post-smile. A clear cycle is observed in standardized scores at an appropriate level for mPCA and in single-level PCA. mPCA can be used to study static shapes and images, as well as dynamic changes in shape. It gave us much insight into the question “what’s in a smile?

What’s in a smile?:initial analyses of dynamic changes in facial shape and appearance

Abstract Single-level principal component analysis (PCA) and multi-level PCA (mPCA) methods are applied here to a set of (2D frontal) facial images from a group of 80 Finnish subjects (34 male; 46 female) with two different facial expressions (smiling and neutral) per subject. Inspection of eigenvalues gives insight into the importance of different factors affecting shapes, including: biological sex, facial expression (neutral versus smiling), and all other variations. Biological sex and facial expression are shown to be reflected in those components at appropriate levels of the mPCA model. Dynamic 3D shape data for all phases of a smile made up a second dataset sampled from 60 adult British subjects (31 male; 29 female). Modes of variation reflected the act of smiling at the correct level of the mPCA model. Seven phases of the dynamic smiles are identified: rest pre-smile, onset 1 (acceleration), onset 2 (deceleration), apex, offset 1 (acceleration), offset 2 (deceleration), and rest post-smile. A clear cycle is observed in standardized scores at an appropriate level for mPCA and in single-level PCA. mPCA can be used to study static shapes and images, as well as dynamic changes in shape. It gave us much insight into the question “what’s in a smile?”