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

Smile and Laugh Expressions Detection Based on Local Minimum Key Points