  In this paper, a smile and laugh facial expression is presented based on
dimension reduction and description process of the key points. The paper has
two main objectives; the first is to extract the local critical points in terms
of their apparent features, and the second is to reduce the system's dependence
on training inputs. To achieve these objectives, three different scenarios on
extracting the features are proposed. First of all, the discrete parts of a
face are detected by local binary pattern method that is used to extract a set
of global feature vectors for texture classification considering various
regions of an input-image face. Then, in the first scenario and with respect to
the correlation changes of adjacent pixels on the texture of a mouth area, a
set of local key points are extracted using the Harris corner detector. In the
second scenario, the dimension reduction of the extracted points of first
scenario provided by principal component analysis algorithm leading to
reduction in computational costs and overall complexity without loss of
performance and flexibility, etc.
