Local Features and 1D-HMMs for Fast and Robust Face Authentication
It has been previously demonstrated that systems based on Hidden Markov Models (HMMs) are suitable for face recognition. The proposed approaches in the literature are either HMMs with one-dimensional (1D-HMMs) or two-dimensional (2D-HMMs) topology. Both have shown some serious drawbacks. The 1D-HMM approaches typically use a whole row (or column) of an image as observation vector and by consequence do not allow horizontal (or vertical) alignment. 2D-HMM approaches present some implementation issues because of the computational cost. In this paper, we propose a 1D-HMM approach which allow the use of local features and we will demonstate the accuracy of this approach on the so-called BANCA database.
Record created on 2006-03-10, modified on 2016-08-08