A sequential pruning strategy for the selection of the number of states in hidden Markov models
Pattern Recognition Letters
Face recognition via the overlapping energy histogram
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An EDBoost algorithm towards robust face recognition in JPEG compressed domain
Image and Vision Computing
K-means clustering algorithm for multimedia applications with flexible HW/SW co-design
Journal of Systems Architecture: the EUROMICRO Journal
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A transform domain approach coupled with HiddenMarkov Model (HMM) for face recognition is presented.JPEG kind of strategy is employed to transform input sub-imagefor training HMMs. DCT transformed vectors of faceimages are used to train ergodic HMM and later for recognition.ORL face database of 40 subjects with 10 imagesper subject is used to evaluate the performance of the proposedmethod. 5 images per subject are used for trainingand the rest 5 for recognition. This method has an accuracyof 99.5%. The results, to the best of knowledge of the authors,give the best recognition percentage as compared toany other method reported so far on ORL face database.