In this paper, a new system for face recognition is proposed, based on hidden Markov models (HMM) and wavelet coding. A sequence of overlapping sub-images is extracted from each face image, computing the wavelet coefficients for each of them. The whole sequence is then modelled by using hidden Markov models. The proposed method is compared with a DCT coefficient-based approach (Kohir et al. (1998)), showing comparable results. By using an accurate model selection procedure, we show that results proposed in Kohir can be improved even more. The obtained results outperform all results presented in the literature on the Olivetti Research Laboratory (ORL) face database, reaching a 100% recognition rate. This performance proves the suitability of HMM to deal with the new JPEG2000 image compression standard.
Using hidden Markov models and wavelets for face recognition
2003-01-01
410896 byte
Conference paper
Electronic Resource
English
Using Hidden Markov Models and Wavelets for Face Recognition
British Library Conference Proceedings | 2003
|Video-Based Face Recognition Using Adaptive Hidden Markov Models
British Library Conference Proceedings | 2003
|Vehicle Recognition Based on Gabor Wavelets Transform and Hidden Markov Model
British Library Conference Proceedings | 2007
|