Harris Operator and K-means Clustering-based Facial Features Localization on Infrared Images / 航天医学与医学工程

ABSTRACT

Objective To develop an image analyzing procedure for automatic localization of facial features on infrared images.Methods An unsupervised local and global features extraction method was adopted for the localization of facial features of frontal view face image.First,a threshold was used to segment the image into foreground and background,and the nose was localized by considering the symmetry of the face.Second,Harris operator was adopted to detect interest points in a rectangular area covering all the facial features,and then local maximum of the interest points were detected.And finally,K-means clustering method was used to cluster the points and obtain the facial features localization.Results The experimental result of 100 images demonstrated that the procedure could automatically localize eyes,nose,mouth,and distinguish the feature areas.Conclusion The proposed infrared image analyzing procedure based on Harris operator and K-means clustering can be used to locate facial features on infrared image more rapidly and reliablely.