Face recognition using Intrinsicfaces
In this paper, we propose a novel face model, called intrinsic face model. Under this model, each face image is divided into three components, i.e., facial commonness difference, individuality difference and intrapersonal difference, to characterize some certain differences conveyed by this image. Then, a new supervised dimensionality reduction technique coined Intrinsic Discriminant Analysis (IDA) is developed. Intrinsic Discriminant Analysis tries to best classify different face images by maximizing the individuality difference, while minimizing the intrapersonal difference. By using perturbation technique to tackle the singularity problem of IDA which occurs frequently in face recognition, we obtain a new appearance-based face recognition method called Intrinsicfaces. A series of experiments to compare our proposed approach with other dimensionality reduction methods are tested on three well-known face databases. Experimental results demonstrate the efficacy of the proposed Intrinsicfaces approach in face recognition.
face recognition dimensionality reduction intrinsic discriminant analysis principal component analysis linear discriminant analysis