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Arumuga Maria Devi, T.
- Security Based Speaker Verification for Lip-Password using Learning Multi-Boosted HMMS
Authors
1 Department of Information Technolgy in Manonmaniam Sundaranar University, Tirunelveli, TamilNadu, IN
2 Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, IN
Source
Digital Image Processing, Vol 7, No 8 (2015), Pagination: 234-241Abstract
Lip password is composed of a password embedded with motions of lip and point out the characteristic of lip motion. To provides security of a speaker verification system by using private password and behavioral biometrics of a lip motion simultaneously. The target speaker saying wrong password then rejected and the target speaker saying correct password then detected. Here a Hidden Markov Model (HMM) learning approach based on multi boosted scheme is presented for a security speaker system. This method first extract the visual features and to characterize each frame. The lip password segmentation algorithm is used for the segmentation of lip sequences. Hidden Markov Models with boosting learning framework contains random subspace method and data sharing scheme. Finally, the lip-password is verified based on verification results provided by all the subunit learned from HMM based multi-boosted scheme and it will check whether the password is spoken by the speaker with the already-recorded password or not.Keywords
Lip Motion, HMM, GMM, RSM, DSS.
- Graph Cut Based Method for Automatic Lung Segmentation for Tuberculosis by using Screening Method in Chest Radiographs
Authors
1 Manonmaniam Sundaranar University, Tirunelveli, TamilNadu, IN
2 Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, IN
3 Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, IN
Source
Digital Image Processing, Vol 7, No 9 (2015), Pagination: 285-291Abstract
In medical imaging technique tuberculosis is an important challenging approach. Most of the peoples affected by the tuberculosis and tuberculosis are a very big disease after the HIV in India. The mortality rate of the peoples is high by affecting tuberculosis. Chest radiographs are also called as chest x ray or CXR. By using graph cut segmentation method is used to extract the lung region and texture and shape features are classified by using binary classifier. The postero anterior is used to automatically detect the tuberculosis. The existing smear microscopy is slow and unreliable. The ROC curve is used to illustrate the performance of the binary classifier. Three terms are classified as follows: Lung segmentation, feature computation, classification. Automated nodule detection is more nature applications of decision support/automation for CXR and CT.