Mathematical Models of the Representation of Faces in Humans
The representation of faces is a crucial function of the human CNS, as demonstrated by the severe social difficulties experienced when people lack this ability (prosopagnosia). However, the precise way in which faces are represented and differentiated from one another is not well understood. This work addresses two substantial issues. Firstly, how is information about faces integrated over time? In chapter 2 a simple model of temporal integration is set forth, based on the statistical technique of exponential smoothing. In chapter 3 results of experiments testing this model are presented, demonstrating the model to be inadequate in certain respects. In particular a systematic bias towards the origin of face space is observed, a phenomenon referred to as ”bowing”. Chapter 4 contains a further model, which aims to show how this bowing could arise from a Bayesian inferential process. The second issue, addressed in chapter 5 of this thesis, is how well human judgements of facial similarity correspond to predictions made using Basel Face Space (BFS), a popular and widely used representation of faces from the field of computer vision. The degree of agreement is quantified using a novel experimental approach, and subsequently salient differences between the biological face space and BFS, including some original findings relating to isotropy or directionality, are demonstrated.
Faces, computer vision, Bayesian inference
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