Indexed on: 18 Mar '16Published on: 22 Oct '15Published in: Signal Processing
Gaussian fields and harmonic functions (GFHF) and flexible manifold embedding (FME) provide effective means to label learning, by virtue of which we can evaluate labels of unknown samples (i.e. samples with unknown labels). When applied to face recognition, they are faced with the challenge that the face image varies with illuminations and facial expressions and poses. Moreover, in face recognition applications, available samples with known labels are almost always not sufficient. Thus it is hard to exploit GFHF or FME to achieve very satisfactory face recognition performance. In this paper, a novel FME algorithm is proposed for face recognition. Our work has two main contributions. Firstly, it devices a score fusion scheme to predict the label of the original unknown sample. Secondly, it obtains mirror images of all original face images and views both mirror images and original face images as available samples. The experimental results demonstrate that algorithm proposed in this paper can perform very well in face recognition.