Face frontalization for Alignment and Recognition
This addresses pose-invariant face recognition for computer vision applications, but it is incremental as it builds on existing methods with a novel formulation.
The paper tackles the problem of joint face landmark localization and frontal face reconstruction using only a small set of frontal images, achieving effective results in pose correction, landmark localization, and pose-invariant face recognition across six databases.
Recently, it was shown that excellent results can be achieved in both face landmark localization and pose-invariant face recognition. These breakthroughs are attributed to the efforts of the community to manually annotate facial images in many different poses and to collect 3D faces data. In this paper, we propose a novel method for joint face landmark localization and frontal face reconstruction (pose correction) using a small set of frontal images only. By observing that the frontal facial image is the one with the minimum rank from all different poses we formulate an appropriate model which is able to jointly recover the facial landmarks as well as the frontalized version of the face. To this end, a suitable optimization problem, involving the minimization of the nuclear norm and the matrix $\ell_1$ norm, is solved. The proposed method is assessed in frontal face reconstruction (pose correction), face landmark localization, and pose-invariant face recognition and verification by conducting experiments on $6$ facial images databases. The experimental results demonstrate the effectiveness of the proposed method.