Batch Face Alignment using a Low-rank GAN
This addresses face alignment for computer vision applications, but it is incremental as it builds on existing GAN and low-rank methods.
The paper tackles the problem of aligning multiple face images of the same person into a normalized form while handling outliers like occlusion and illumination, achieving higher accuracy and efficiency than existing methods in experiments on real-world databases.
This paper studies the problem of aligning a set of face images of the same individual into a normalized image while removing the outliers like partial occlusion, extreme facial expression as well as significant illumination variation. Our model seeks an optimal image domain transformation such that the matrix of misaligned images can be decomposed as the sum of a sparse matrix of noise and a rank-one matrix of aligned images. The image transformation is learned in an unsupervised manner, which means that ground-truth aligned images are unnecessary for our model. Specifically, we make use of the remarkable non-linear transforming ability of generative adversarial network(GAN) and guide it with low-rank generation as well as sparse noise constraint to achieve the face alignment. We verify the efficacy of the proposed model with extensive experiments on real-world face databases, demonstrating higher accuracy and efficiency than existing methods.