Local Features and 1D-HMMs for Fast and Robust Face Authentication
| Type of publication: | Idiap-RR |
| Citation: | cardinaux05-17 |
| Number: | Idiap-RR-17-2005 |
| Year: | 2005 |
| Institution: | IDIAP |
| Abstract: | 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. |
| Userfields: | ipdmembership={vision}, |
| Keywords: | |
| Projects: |
Idiap |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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