Issue No. 02 - Feb. (2013 vol. 35)
H. Mohammadzade , Dept. of Electr. & Comput. Eng., Univ. of Toronto, Toronto, ON, Canada
D. Hatzinakos , Dept. of Electr. & Comput. Eng., Univ. of Toronto, Toronto, ON, Canada
The common approach for 3D face recognition is to register a probe face to each of the gallery faces and then calculate the sum of the distances between their points. This approach is computationally expensive and sensitive to facial expression variation. In this paper, we introduce the iterative closest normal point method for finding the corresponding points between a generic reference face and every input face. The proposed correspondence finding method samples a set of points for each face, denoted as the closest normal points. These points are effectively aligned across all faces, enabling effective application of discriminant analysis methods for 3D face recognition. As a result, the expression variation problem is addressed by minimizing the within-class variability of the face samples while maximizing the between-class variability. As an important conclusion, we show that the surface normal vectors of the face at the sampled points contain more discriminatory information than the coordinates of the points. We have performed comprehensive experiments on the Face Recognition Grand Challenge database, which is presently the largest available 3D face database. We have achieved verification rates of 99.6 and 99.2 percent at a false acceptance rate of 0.1 percent for the all versus all and ROC III experiments, respectively, which, to the best of our knowledge, have seven and four times less error rates, respectively, compared to the best existing methods on this database.
Face, Three dimensional displays, Nose, Face recognition, Databases, Vectors, Principal component analysis,LDA, Three-dimensional, face recognition, expression variation, point correspondence, 3D registration, surface normal vector
H. Mohammadzade, D. Hatzinakos, "Iterative Closest Normal Point for 3D Face Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. , pp. 381-397, Feb. 2013, doi:10.1109/TPAMI.2012.107