Smile and Laugh Expressions Detection Based on Local Minimum Key Points

TL;DR

This paper proposes a method for detecting smile and laugh expressions by extracting local key points and reducing system dependence on training data, using texture analysis and dimension reduction techniques.

Contribution

It introduces a novel approach combining local binary pattern features, Harris corner detection, and PCA for efficient facial expression recognition.

Findings

Effective detection of smile and laugh expressions.

Reduced computational complexity with maintained accuracy.

Less reliance on extensive training data.

Abstract

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.