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A comparison of discrete and continuous output modeling techniques for a pseudo-2D hidden Markov model face recognition system

12 years 1 months ago
A comparison of discrete and continuous output modeling techniques for a pseudo-2D hidden Markov model face recognition system
Face recognition has become an important topic within the field of pattern recognition and computer vision. In this field a number of different approaches to feature extraction, modeling and classification techniques have been tested. However, many questions concerning the optimal modeling techniques for high performance face recognition are still open. The face recognition system developed by our research group uses a Discrete Cosine Transform (DCT) combined with the use of Pseudo 2D Hidden Markov Models (P2DHMM). In the past our system used continuous probability density functions and was tested on a smaller database. This paper addresses the question if there is a major difference in recognition performance with discrete production probabilities compared to continuous ones. Therefore the system is tested using a larger subset of the FERET database. We will show that we are able to achieve higher recognition scores and an improvement concerning the computation speed by using discret...
Frank Wallhoff, Stefan Eickeler, Gerhard Rigoll
Added 25 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2001
Where ICIP
Authors Frank Wallhoff, Stefan Eickeler, Gerhard Rigoll
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