The variation of pose, illumination and expression makes face recognition
still a challenging problem. As a pre-processing in holistic approaches, faces
are usually aligned by eyes. The proposed method tries to perform a pixel
alignment rather than eye-alignment by mapping the geometry of faces to a
reference face while keeping their own textures. The proposed geometry
alignment not only creates a meaningful correspondence among every pixel of all
faces, but also removes expression and pose variations effectively. The
geometry alignment is performed pixel-wise, i.e., every pixel of the face is
corresponded to a pixel of the reference face. In the proposed method, the
information of intensity and geometry of faces are separated properly, trained
by separate classifiers, and finally fused together to recognize human faces.
Experimental results show a great improvement using the proposed method in
comparison to eye-aligned recognition. For instance, at the false acceptance
rate of 0.001, the recognition rates are respectively improved by 24% and 33%
in Yale and AT&T datasets. In LFW dataset, which is a challenging big dataset,
improvement is 20% at FAR of 0.1.