A case for the average-half-face in 2D and 3D for face recognition

We observe that the human face is inherently symmetric and we would like to exploit this symmetry in face recognition. The average-half-face has been previously shown to do just that for a set of 3D faces when using eigenfaces for recognition. We build upon that work and present a comparison of the use of the average-half-face to the use of the original full face with 6 different algorithms applied to two- and three-dimensional (2D and 3D) databases. The average-half-face is constructed from the full frontal face image in two steps; first the face image is centered and divided in half and then the two halves are averaged together (reversing the columns of one of the halves). The resulting average-half-face is then used as the input for face recognition algorithms. Previous work has shown that the accuracy of 3D face recognition using eigenfaces with the average-half-face is significantly better than using the full face. We compare the results using the average-half-face and the full face using six face recognition methods; eigenfaces, multi-linear principal components analysis (MPCA), MPCA with linear discriminant analysis (MPCALDA), Fisherfaces (LDA), independent component analysis (ICA), and support vector machines (SVM). We utilize two well-known 2D face database as well as a 3D face database for the comparison. Our results show that in most cases it is superior to employ the average-half-face for frontal face recognition. The consequences of this discovery may result in substantial savings in storage and computation time.