Fully automated facial symmetry axis detection in frontal color images

Abstract

We consider the problem of automatically detecting a facial symmetry axis in what we will call a standard human face image (acquired when the subject is looking directly into the camera, in front of a clean gray background under controlled illumination). Images of this kind are encountered in face recognition scenarios; this detection should facilitate more sophisticated facial image processing. The proposed method is based on GLDH (gray level difference histogram) analysis and consists of three components: (1) the face region detection stage crops an approximate face region out of the background, (2) symmetry detection discovers a vertical axis to optimally bisect the region of interest, assuming it is bilaterally symmetric, and (3) orientation adjustment aligns the angle of the symmetry axis with the orientation of the face. An implementation of the method is described, and results are presented. This detector's robust performance is evidenced by its success finding symmetry axes in more than 7,500 images collected from 600 distinct subjects. One of our method's most noteworthy contributions is that, according to our experimental results, many of the automatically detected axes are more accurate than the reference axes. Our automated detector is a powerful tool because it is not as susceptible to human error as its manual counterpart and, as the first application of its kind, it could potentially serve as a new biometric.