A transform domain approach coupled with Hidden Markov Model (HMM) for face recognition is presented. JPEG kind of strategy is employed to transform input subimage for training HMMs. DCT transformed vectors efface images are used to train ergodic HMM and later for recognition. ORL face database of 40 subjects with 10 images per subject is used to evaluate the performance of the proposed method. 5 images per subject are used for training and the rest 5 for recognition. This method has an accuracy of 99.5%. The results, to the best of knowledge of the authors, give the best recognition percentage as compared to any other method reported so far on ORL face database
Abstract – Face recognition from still images and video sequences is emerging as an active research ...
A new hidden Markov model (HMM) based feature generation scheme is proposed for face recognition (FR...
International audienceIn this paper we present a new architecture for face recognition with a single...
A transform domain approach coupled with Hidden Markov Model (HMM) for face recognition is presented...
The paper combines DCT (discrete cosine transform) and HMM (hidden Markov model) to realise a face r...
A transform domain face recognition approach is presented. The DCT is coupled with the HMM to achiev...
The work presented in this paper focuses on the use of Hidden Markov Models for face recognition. A ...
This dissertation introduces work on face recognition using a novel technique based on Hidden Marko...
While traditional face recognition is typically based on still images, face recognition from video s...
While traditional face recognition is typically based on still images, face recognition from video s...
While traditional face recognition is typically based on still images, face recognition from video s...
While traditional face recognition is typically based on still images, face recognition from video s...
In most real world applications, multiple image samples of individuals are not easy to collate for r...
Abstract Face recognition from an image or video sequences is emerging as an active research area wi...
Face recognition has been known as one of key applications to build high-performance surveillance or...
Abstract – Face recognition from still images and video sequences is emerging as an active research ...
A new hidden Markov model (HMM) based feature generation scheme is proposed for face recognition (FR...
International audienceIn this paper we present a new architecture for face recognition with a single...
A transform domain approach coupled with Hidden Markov Model (HMM) for face recognition is presented...
The paper combines DCT (discrete cosine transform) and HMM (hidden Markov model) to realise a face r...
A transform domain face recognition approach is presented. The DCT is coupled with the HMM to achiev...
The work presented in this paper focuses on the use of Hidden Markov Models for face recognition. A ...
This dissertation introduces work on face recognition using a novel technique based on Hidden Marko...
While traditional face recognition is typically based on still images, face recognition from video s...
While traditional face recognition is typically based on still images, face recognition from video s...
While traditional face recognition is typically based on still images, face recognition from video s...
While traditional face recognition is typically based on still images, face recognition from video s...
In most real world applications, multiple image samples of individuals are not easy to collate for r...
Abstract Face recognition from an image or video sequences is emerging as an active research area wi...
Face recognition has been known as one of key applications to build high-performance surveillance or...
Abstract – Face recognition from still images and video sequences is emerging as an active research ...
A new hidden Markov model (HMM) based feature generation scheme is proposed for face recognition (FR...
International audienceIn this paper we present a new architecture for face recognition with a single...