A face recognition system ought to read out information about the identity, facial expression and invariant properties of faces, such as sex and race. A current debate is whether separate neural units in the brain deal with these face properties individually or whether a single neural unit processes in parallel all aspects of faces. While the focus of studies has been directed toward the processing of identity and facial expression, little research exists on the processing of invariant aspects of faces. In a theoretical framework we tested whether a system can deal with identity in combination with sex, race or facial expression using the same underlying mechanism. We used dimension reduction to describe how the representational face space organizes face properties when trained on different aspects of faces. When trained to learn identities, the system not only successfully recognized identities, but also was immediately able to classify sex and race, suggesting that no additional system for the processing of invariant properties is needed. However, training on identity was insufficient for the recognition of facial expressions and vice versa. We provide a theoretical approach on the interconnection of invariant facial properties and the separation of variant and invariant facial properties.
|Publication status||Published - Feb 2 2016|
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