Compensating for Ensemble-Specific Effects When Building Facial Models

When attempting to code faces for modeling or recognition, estimates of dimensions are typically obtained from an ensemble. These tend to be significantly sub-optimal. Firstly, ensembles are rarely balanced with regard to identity and expression. This can be overcome by dividing the ensemble by type of variation and rotating sub-spaces relative to one another. Secondly, each face contains both predictable and non-predictable qualities; only the predictable aspects are useful for defining coding systems for other faces

Markov Fields for Recognition Derived from Facial Texture Error

When attempting to code faces for modelling or recognition, estimates of dimensions are typically obtained from an ensemble. These tend to be significantly sub-optimal. Each face contains both predictable and non-predictable qualitie